com.irurueta.numerical.robust.RobustEstimator<T>

com.irurueta.numerical.robust.PROMedSRobustEstimator<T>

Type Parameters:: T - type of object to be estimated.

public class PROMedSRobustEstimator<T> extends RobustEstimator<T>

This class implements PROMedS (PROgressive least Median Sample) algorithm to robustly estimate a data model. This algorithm is a mixture between LMedS and PROSAC, taking the best of both. Firstly, it has the advantage that no threshold is required to be set beforehand, the same as LMedS. Threshold to determine inliers is computed dynamically, which helps for an easier setup that is problem independent and depending on the accuracy of the inliers, results will be more accurate than RANSAC or PROSAC, just the same as LMedS. On the other hand, if certain information about the quality of the samples is available, as in PROSAC, the algorithm takes advantage of this additional information to prioritize the samples with higher quality in order to find a solution much faster than RANSAC or LMedS. Finally, if by any chance a threshold to determine inliers is also used, the algorithm will try to get the solution that better fits in a pure median of residuals model or in a threshold based one to determine inliers. Hence, PROMedS can be as fast as PROSAC (which is typically about 100x faster than RANSAC or LMedS), can obtain the same accuracy as LMedS (which can be much better than RANSAC or PROSAC in certain scenarios), and has an easier setup, which is problem independent because no threshold is required to be known beforehand although one can be provided as well.

Nested Class Summary

Nested Classes

Modifier and Type

Class

Description

static class

PROMedSRobustEstimator.PROMedSInliersData

Contains data related to inliers estimated in one iteration.
Field Summary

Fields

Modifier and Type

Field

Description

private PROMedSRobustEstimator.PROMedSInliersData

bestInliersData

Data related to inliers found for best result.

private T

bestResult

Best solution that has been found so far during an estimation.

private double

beta

beta is the probability that a match is declared inlier by mistake, i.e.

static final double

CHI_SQUARED

Chi squared.

private double

confidence

Amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).

static final double

DEFAULT_BETA

Defines the default value for beta, which is the probability that a match is declared inlier by mistake, i.e.

static final double

DEFAULT_CONFIDENCE

Constant defining default confidence of the estimated result, which is 99%.

static final double

DEFAULT_ETA0

Defines the default value for the maximum probability that a solution with more than inliersNStar in U_nStar exist and was not found after k samples.

static final double

DEFAULT_INLIER_FACTOR

Default factor to normalize threshold to determine inliers.

static final int

DEFAULT_MAX_ITERATIONS

Default maximum allowed number of iterations.

static final double

DEFAULT_MAX_OUTLIERS_PROPORTION

Default maximum allowed outliers proportion in the input data.

static final boolean

DEFAULT_STOP_THRESHOLD_ENABLED

Indicates whether the algorithm must stop prematurely when dynamically computed threshold using median of residuals has a value lower than provided threshold in listener.

static final boolean

DEFAULT_USE_INLIER_THRESHOLD

Indicates whether the inlier threshold will be used to find inliers along with their median of residuals.

private double

eta0

eta0 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%).

private double

inlierFactor

Factor to normalize threshold to determine inliers.

private int

iters

Number of iterations to be done to obtain required confidence.

static final double

MAX_BETA

Maximum allowed value for beta.

static final double

MAX_CONFIDENCE

Maximum allowed confidence value.

static final double

MAX_ETA0

Maximum allowed value for eta0.

static final double

MAX_MAX_OUTLIERS_PROPORTION

Maximum allowed value for maximum allowed outliers proportion in the input data.

private int

maxIterations

Maximum allowed number of iterations.

private double

maxOutliersProportion

In this implementation, PROSAC won't stop before having reached the corresponding inliers rate on the complete data set.

static final double

MIN_BETA

Minimum allowed value for beta.

static final double

MIN_CONFIDENCE

Minimum allowed confidence value.

static final double

MIN_ETA0

Minimum allowed value for eta0.

static final double

MIN_INLER_FACTOR

Minimum allowed value for inlier factor.

static final int

MIN_ITERATIONS

Minimum allowed number of iterations.

static final double

MIN_MAX_OUTLIERS_PROPORTION

Minimum allowed value for maximum allowed outliers proportion in the input data.

static final double

STD_CONSTANT

Constant to estimate standard deviation of residuals based on their median.

private boolean

stopThresholdEnabled

Indicates whether the algorithm must stop prematurely when dynamically computed threshold using median of residuals has a value lower than provided threshold in listener.

private SubsetSelector

subsetSelector

Instance in charge of picking random subsets of samples.

private boolean

useInlierThresholds

Flag indicating whether thresholds to determine inliers are used, or if only median of residuals is used.

Fields inherited from class com.irurueta.numerical.robust.RobustEstimator
DEFAULT_PROGRESS_DELTA, listener, locked, MAX_PROGRESS_DELTA, MIN_PROGRESS_DELTA, progressDelta
Constructor Summary

Constructors

Constructor

Description

PROMedSRobustEstimator()

Constructor.

PROMedSRobustEstimator(PROMedSRobustEstimatorListener<T> listener)

Constructor with listener.
Method Summary

Modifier and Type

Method

Description

private static <T> void

computeInliers(T iterResult, int subsetSize, double inlierFactor, boolean useInlierThresholds, double inlierThreshold, double[] residualsTemp, LMedSRobustEstimatorListener<T> listener, com.irurueta.sorting.Sorter<Double> sorter, PROMedSRobustEstimator.PROMedSInliersData inliersData)

Computes inliers data for current iteration.

private static int

computeIterations(double probInlier, int subsetSize, double confidence)

Computes number of required iterations to achieve required confidence with current probability of inlier and sample subset size.

private static int[]

computeSortedQualityIndices(double[] qualityScores)

Obtains indices of samples corresponding to samples ordered in descending quality scores.

T

estimate()

Robustly estimates an instance of T.

protected PROMedSRobustEstimator.PROMedSInliersData

getBestInliersData()

Returns data related to inliers found for best result.

T

getBestResult()

Returns best solution that has been found so far during an estimation.

double

getBeta()

Returns beta, which is the probability that a match is declared inlier by mistake, i.e.

double

getConfidence()

Returns amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).

double

getEta0()

Return 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%).

double

getInlierFactor()

Returns factor to normalize or adjust threshold to determine inliers.

InliersData

getInliersData()

Returns data about inliers once estimation has been done.

int

getMaxIterations()

Maximum allowed number of iterations.

double

getMaxOutliersProportion()

Returns maximum allowed outliers proportion in the input data.

RobustEstimatorMethod

getMethod()

Returns method being used for robust estimation.

int

getNIters()

Returns number of iterations to be done to obtain required confidence.

private static int

imin(int subsetSize, int sampleSize, double beta)

Non randomness states that i-m (where i is the cardinal of the set of inliers for a wrong model) follows the binomial distribution B(n,beta).

boolean

isReady()

Indicates if estimator is ready to start the estimation process.

boolean

isStopThresholdEnabled()

Returns 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.

boolean

isUseInlierThresholds()

Returns flag indicating whether thresholds to determine inliers are used, or if only median of residuals is used.

private void

keepInliersData(PROMedSRobustEstimator.PROMedSInliersData inliersData, int totalSamples)

Keeps inliers data stored and initializes a new one with proper initial values.

private static void

reverse(int[] array)

Reverses provided array.

void

setBeta(double beta)

Sets beta, which is the probability that a match is declared inlier by mistake, i.e.

void

setConfidence(double confidence)

Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).

void

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

setInlierFactor(double inlierFactor)

Sets factor to normalize or adjust threshold to determine inliers.

void

setMaxIterations(int maxIterations)

Sets maximum allowed number of iterations.

void

setMaxOutliersProportion(double maxOutliersProportion)

Sets maximum allowed outliers proportion in the input data.

void

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

setUseInlierThresholds(boolean useInlierThresholds)

Sets flag indicating whether thresholds to determine inliers are used, or if only median of residuals is used.

private static void

transformIndices(int[] subsetIndices, int[] sortedIndices, int[] transformedSubsetIndices)

Transforms indices picked by the subset selector into the indices where samples are actually localed by taking into account their original position before sorting quality scores.

Methods inherited from class com.irurueta.numerical.robust.RobustEstimator
getListener, getProgressDelta, isListenerAvailable, isLocked, setListener, setProgressDelta

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

Field Details
- DEFAULT_CONFIDENCE
  
  public static final double DEFAULT_CONFIDENCE
  
  Constant defining default confidence of the estimated result, which is 99%. This means that with a probability of 99% estimation will be accurate because chosen sub-samples will be inliers.
  See Also:
  
  Constant Field Values
- DEFAULT_MAX_ITERATIONS
  
  public static final int DEFAULT_MAX_ITERATIONS
  
  Default maximum allowed number of iterations.
  See Also:
  
  Constant Field Values
- MIN_CONFIDENCE
  
  public static final double MIN_CONFIDENCE
  
  Minimum allowed confidence value.
  See Also:
  
  Constant Field Values
- MAX_CONFIDENCE
  
  public static final double MAX_CONFIDENCE
  
  Maximum allowed confidence value.
  See Also:
  
  Constant Field Values
- MIN_ITERATIONS
  
  public static final int MIN_ITERATIONS
  
  Minimum allowed number of iterations.
  See Also:
  
  Constant Field Values
- DEFAULT_STOP_THRESHOLD_ENABLED
  
  public static final boolean DEFAULT_STOP_THRESHOLD_ENABLED
  
  Indicates whether the algorithm must stop prematurely when dynamically computed threshold using median of residuals has a value lower than provided threshold in listener. When this flag is enabled accuracy of PROMedS worsens to a lever similar to PROSAC but the number of iterations is reduced (i.e. less computational cost). If more accuracy is desired at the expense of some additional computation cost, then disable this flag. By default, stop threshold is enabled, so that computational cost is similar to RANSAC and only accuracy gets better if inliers are more accurate.
  See Also:
  
  Constant Field Values
- DEFAULT_MAX_OUTLIERS_PROPORTION
  
  public static final double DEFAULT_MAX_OUTLIERS_PROPORTION
  
  Default maximum allowed outliers proportion in the input data. This is used do determine the number of required iterations.
  See Also:
  
  Constant Field Values
- MIN_MAX_OUTLIERS_PROPORTION
  
  public static final double MIN_MAX_OUTLIERS_PROPORTION
  
  Minimum allowed value for maximum allowed outliers proportion in the input data.
  See Also:
  
  Constant Field Values
- MAX_MAX_OUTLIERS_PROPORTION
  
  public static final double MAX_MAX_OUTLIERS_PROPORTION
  
  Maximum allowed value for maximum allowed outliers proportion in the input data.
  See Also:
  
  Constant Field Values
- DEFAULT_ETA0
  
  public static final double DEFAULT_ETA0
  
  Defines the default value for the maximum probability that a solution with more than inliersNStar in U_nStar exist and was not found after k samples.
  See Also:
  
  Constant Field Values
- MIN_ETA0
  
  public static final double MIN_ETA0
  
  Minimum allowed value for eta0.
  See Also:
  
  Constant Field Values
- MAX_ETA0
  
  public static final double MAX_ETA0
  
  Maximum allowed value for eta0.
  See Also:
  
  Constant Field Values
- DEFAULT_BETA
  
  public static final double DEFAULT_BETA
  
  Defines the default value for beta, which is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface. The inlier surface is a disc with radius 1.96s for homography/displacement computation, or a band with width 1.96*s*2 for epipolar geometry (s is the detection noise), and the total surface is the surface of the image.
  See Also:
  
  Constant Field Values
- MIN_BETA
  
  public static final double MIN_BETA
  
  Minimum allowed value for beta.
  See Also:
  
  Constant Field Values
- MAX_BETA
  
  public static final double MAX_BETA
  
  Maximum allowed value for beta.
  See Also:
  
  Constant Field Values
- DEFAULT_INLIER_FACTOR
  
  public static final double DEFAULT_INLIER_FACTOR
  
  Default factor to normalize threshold to determine inliers. This factor can be used to increase or lower the dynamically computed threshold so that the algorithm becomes more or less accurate. The stricter the threshold (lower factor), the more time the algorithm will need to converge, if it can converge. By default, the factor is 1.0, which makes the threshold to be computed as the median of residuals.
  See Also:
  
  Constant Field Values
- MIN_INLER_FACTOR
  
  public static final double MIN_INLER_FACTOR
  
  Minimum allowed value for inlier factor.
  See Also:
  
  Constant Field Values
- DEFAULT_USE_INLIER_THRESHOLD
  
  public static final boolean DEFAULT_USE_INLIER_THRESHOLD
  
  Indicates whether the inlier threshold will be used to find inliers along with their median of residuals.
  See Also:
  
  Constant Field Values
- STD_CONSTANT
  
  public static final double STD_CONSTANT
  
  Constant to estimate standard deviation of residuals based on their median.
  See Also:
  
  Constant Field Values
- CHI_SQUARED
  
  public static final double CHI_SQUARED
  
  Chi squared.
  See Also:
  
  Constant Field Values
- confidence
  
  private double confidence
  
  Amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%). The amount of confidence indicates the probability that the estimated result is correct. Usually this value will be close to 1.0, but not exactly 1.0.
- maxIterations
  
  private int maxIterations
  
  Maximum allowed number of iterations. When the maximum number of iterations is exceeded, result will not be available, however an approximate result will be available for retrieval.
- maxOutliersProportion
  
  private double maxOutliersProportion
  
  In this implementation, PROSAC won't stop before having reached the corresponding inliers rate on the complete data set. Maximum allowed outliers proportion in the input data: used to compute nIters (can be as high as 0.95).
- eta0
  
  private double eta0
  
  eta0 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%).
- beta
  
  private double beta
  
  beta is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface. The inlier surface is a disc with radius 1.96s for homography/displacement computation, or a band with width 1.96s*2 for epipolar geometry (s is the detection noise), and the total surface is the surface of the image YOU MUST ADJUST THIS VALUE, DEPENDING ON YOUR PROBLEM!.
- subsetSelector
  
  private SubsetSelector subsetSelector
  
  Instance in charge of picking random subsets of samples.
- iters
  
  private int iters
  
  Number of iterations to be done to obtain required confidence.
- bestResult
  
  private T bestResult
  
  Best solution that has been found so far during an estimation.
- bestInliersData
  
  private PROMedSRobustEstimator.PROMedSInliersData bestInliersData
  
  Data related to inliers found for best result.
- stopThresholdEnabled
  
  private boolean stopThresholdEnabled
  
  Indicates whether the algorithm must stop prematurely when dynamically computed threshold using median of residuals has a value lower than provided threshold in listener. When this flag is enabled accuracy of PROMedS worsens to a lever similar to PROSAC but the number of iterations is reduced (i.e. less computational cost). If more accuracy is desired at the expense of some additional computation cost, then disable this flag.
- inlierFactor
  
  private double inlierFactor
  
  Factor to normalize threshold to determine inliers. This factor can be used to increase or lower the dynamically computed threshold so that the algorithm becomes more or less accurate. The stricter the threshold (lower factor), the more time the algorithm will need to converge, if it can converge. By default, the factor is 1.0, which makes the threshold to be computed as the median of residuals.
- useInlierThresholds
  
  private boolean useInlierThresholds
  
  Flag indicating whether thresholds to determine inliers are used, or if only median of residuals is used. When true, the algorithm will try to fit the best model, otherwise only median of residuals will be used.
Constructor Details
- PROMedSRobustEstimator
  
  public PROMedSRobustEstimator()
  
  Constructor.
- PROMedSRobustEstimator
  
  public PROMedSRobustEstimator(PROMedSRobustEstimatorListener<T> listener)
  
  Constructor with listener.
  
  Parameters:
  
  listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes, as well as in charge of picking samples and doing per-iteration estimations.
Method Details
- getConfidence
  
  public double getConfidence()
  
  Returns amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%). The amount of confidence indicates the probability that the estimated result is correct. Usually this value will be close to 1.0, but not exactly 1.0.
  
  Returns:
  
  amount of confidence as a value between 0.0 and 1.0.
- setConfidence
  
  public void setConfidence(double confidence) throws LockedException
  
  Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%). The amount of confidence indicates the probability that the estimated result is correct. Usually this value will be close to 1.0, but not exactly 1.0.
  
  Parameters:
  
  confidence - confidence to be set as a value between 0.0 and 1.0.
  
  Throws:
  
  IllegalArgumentException - if provided value is not between 0.0 and 1.0.
  
  LockedException - if this estimator is locked because an estimation is being computed.
- getMaxIterations
  
  public int getMaxIterations()
  
  Maximum allowed number of iterations. When the maximum number of iterations is exceeded, result will not be available, however an approximate result will be available for retrieval.
  
  Returns:
  
  maximum allowed number of iterations.
- setMaxIterations
  
  public void setMaxIterations(int maxIterations) throws LockedException
  
  Sets maximum allowed number of iterations. When the maximum number of iterations is exceeded, result will not be available, however an approximate result will be available for retrieval.
  
  Parameters:
  
  maxIterations - maximum allowed number of iterations to be set.
  
  Throws:
  
  IllegalArgumentException - if provided value is less than 1.
  
  LockedException - if this estimator is locked because an estimation is being computed.
- isStopThresholdEnabled
  
  public boolean isStopThresholdEnabled()
  
  Returns 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. When this flag is enabled accuracy of PROMedS worsens to a lever similar to PROSAC but the number of iterations is reduced (i.e. less computational cost). If more accuracy is desired at the expense of some additional computation cost, then disable this flag.
  
  Returns:
  
  true if stop threshold is enabled, false otherwise.
- setStopThresholdEnabled
  
  public void setStopThresholdEnabled(boolean stopThresholdEnabled) throws LockedException
  
  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. When this flag is enabled accuracy of PROMedS worsens to a lever similar to PROSAC but the number of iterations is reduced (i.e. less computational cost). If more accuracy is desired at the expense of some additional computation cost, then disable this flag.
  
  Parameters:
  
  stopThresholdEnabled - true if stop threshold is enabled, false otherwise.
  
  Throws:
  
  LockedException - if this estimator is locked because an estimation is being computed.
- getInlierFactor
  
  public double getInlierFactor()
  
  Returns factor to normalize or adjust threshold to determine inliers. This factor can be used to increase or lower the dynamically computed threshold so that the algorithm becomes more or less accurate. The stricter the threshold (lower factor), the more time the algorithm will need to converge, if it can converge. By default, the factor is 1.0, which makes the threshold to be computed as the median of residuals.
  
  Returns:
  
  factor to normalize threshold to determine inliers.
- setInlierFactor
  
  public void setInlierFactor(double inlierFactor) throws LockedException
  
  Sets factor to normalize or adjust threshold to determine inliers. This factor can be used to increase or lower the dynamically computed threshold so that the algorithm becomes more or less accurate. The stricter the threshold (lower factor), the more time the algorithm will need to converge, if it can converge. By default, the factor is 1.0, which makes the threshold to be computed as the median of residuals.
  
  Parameters:
  
  inlierFactor - inlier factor to be set.
  
  Throws:
  
  IllegalArgumentException - if provided value is less or equal than 0.0.
  
  LockedException - if this estimator is locked because an estimation is being computed.
- isUseInlierThresholds
  
  public boolean isUseInlierThresholds()
  
  Returns flag indicating whether thresholds to determine inliers are used, or if only median of residuals is used. When true, the algorithm will try to fit the best model, otherwise only median of residuals will be used.
  
  Returns:
  
  true if best model is used (threshold or median), otherwise only median of residuals will be used.
- setUseInlierThresholds
  
  public void setUseInlierThresholds(boolean useInlierThresholds) throws LockedException
  
  Sets flag indicating whether thresholds to determine inliers are used, or if only median of residuals is used. When true, the algorithm will try to fit the best model, otherwise only median of residuals will be used.
  
  Parameters:
  
  useInlierThresholds - true if best model is used (threshold or median), oitherwise only median of residuals will be used.
  
  Throws:
  
  LockedException - if this estimator is locked because an estimation is being computed.
- getMaxOutliersProportion
  
  public double getMaxOutliersProportion()
  
  Returns maximum allowed outliers proportion in the input data. This is used to compute number of iterations to be done (nIters). It typically can be as high as 0.95. Higher values, up to 1 are possible but not recommended. In this implementation, PROSAC won't stop before having reached the corresponding inliers rate on the complete data set.
  
  Returns:
  
  maximum allowed outliers proportion in the input data.
- setMaxOutliersProportion
  
  public void setMaxOutliersProportion(double maxOutliersProportion) throws LockedException
  
  Sets maximum allowed outliers proportion in the input data. This is used to compute number of iterations to be done (nIters). It typically can be as high as 0.95. Higher values, up to 1 are possible but not recommended. In this implementation, PROSAC won't stop before having reached the corresponding inliers rate on the complete data set.
  
  Parameters:
  
  maxOutliersProportion - maximum allowed outliers proportion in the input data.
  
  Throws:
  
  IllegalArgumentException - if provided value is less than 0.0 or greater than 1.0.
  
  LockedException - if this estimator is locked because an estimation is being computed.
- getEta0
  
  public double getEta0()
  
  Return 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%).
  
  Returns:
  
  eta0 value.
- setEta0
  
  public void setEta0(double eta0) throws LockedException
  
  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%).
  
  Parameters:
  
  eta0 - eta0 value to be set.
  
  Throws:
  
  IllegalArgumentException - if provided value is less than 0.0 or greater than 1.0.
  
  LockedException - if this estimator is locked because an estimation is being computed.
- getBeta
  
  public double getBeta()
  
  Returns beta, which is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface. The inlier surface is a disc with radius 1.96s for homography/displacement computation, or a band with width 1.96s*2 for epipolar geometry (s is the detection noise), and the total surface is the surface of the image YOU MUST ADJUST THIS VALUE, DEPENDING ON YOUR PROBLEM!
  
  Returns:
  
  beta value.
- setBeta
  
  public void setBeta(double beta) throws LockedException
  
  Sets beta, which is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface. The inlier surface is a disc with radius 1.96s for homography/displacement computation, or a band with width 1.96s*2 for epipolar geometry (s is the detection noise), and the total surface is the surface of the image YOU MUST ADJUST THIS VALUE, DEPENDING ON YOUR PROBLEM!
  
  Parameters:
  
  beta - beta value to be set.
  
  Throws:
  
  IllegalArgumentException - if provided value is less than 0.0 or greater than 1.0.
  
  LockedException - if this estimator is locked because an estimation is being computed.
- getNIters
  
  public int getNIters()
  
  Returns number of iterations to be done to obtain required confidence. This does not need to be equal to the actual number of iterations the algorithm finally required to obtain a solution.
  
  Returns:
  
  number of iterations to be done to obtain required confidence.
- getBestResult
  
  public T getBestResult()
  
  Returns best solution that has been found so far during an estimation.
  
  Returns:
  
  best solution that has been found so far during an estimation.
- getBestInliersData
  
  protected PROMedSRobustEstimator.PROMedSInliersData getBestInliersData()
  
  Returns data related to inliers found for best result.
  
  Returns:
  
  data related to inliers found for best result.
- isReady
  
  public boolean isReady()
  
  Indicates if estimator is ready to start the estimation process.
  
  Overrides:
  
  isReady in class RobustEstimator<T>
  
  Returns:
  
  true if ready, false otherwise.
- estimate
  
  public T estimate() throws LockedException, NotReadyException, RobustEstimatorException
  
  Robustly estimates an instance of T.
  
  Specified by:
  
  estimate in class RobustEstimator<T>
  
  Returns:
  
  estimated object.
  
  Throws:
  
  LockedException - if robust estimator is locked.
  
  NotReadyException - if provided input data is not enough to start the estimation.
  
  RobustEstimatorException - if estimation fails for any reason (i.e. numerical instability, no solution available, etc).
- getInliersData
  
  public InliersData getInliersData()
  
  Returns data about inliers once estimation has been done.
  
  Specified by:
  
  getInliersData in class RobustEstimator<T>
  
  Returns:
  
  data about inliers or null if estimation has not been done.
- getMethod
  
  public RobustEstimatorMethod getMethod()
  
  Returns method being used for robust estimation.
  
  Specified by:
  
  getMethod in class RobustEstimator<T>
  
  Returns:
  
  method being used for robust estimation.
- transformIndices
  
  private static void transformIndices(int[] subsetIndices, int[] sortedIndices, int[] transformedSubsetIndices)
  
  Transforms indices picked by the subset selector into the indices where samples are actually localed by taking into account their original position before sorting quality scores.
  
  Parameters:
  
  subsetIndices - indices picked by the subset selector. These are positions after sorting. Must have the subset length.
  
  sortedIndices - indices relating sorted positions to their original positions. Each position i-th in the array refers to the original position before sorting. Must have the number of samples length.
  
  transformedSubsetIndices - array where result is stored. Must have the subset length.
- computeInliers
  
  private static <T> void computeInliers(T iterResult, int subsetSize, double inlierFactor, boolean useInlierThresholds, double inlierThreshold, double[] residualsTemp, LMedSRobustEstimatorListener<T> listener, com.irurueta.sorting.Sorter<Double> sorter, PROMedSRobustEstimator.PROMedSInliersData inliersData)
  
  Computes inliers data for current iteration.
  
  Type Parameters:
  
  T - type of result to be estimated.
  
  Parameters:
  
  iterResult - result to be tested on current iteration.
  
  subsetSize - subset sample size to be picked on each iteration.
  
  inlierFactor - factor to adjust threshold to determine whether samples are inliers or not.
  
  useInlierThresholds - true to use thresholds to determine inliers, false otherwise.
  
  inlierThreshold - threshold to determine which samples are inliers.
  
  residualsTemp - temporal array to store residuals, since median computation requires modifying the original array.
  
  listener - listener to obtain residuals for samples.
  
  sorter - sorter instance to compute median of residuals.
  
  inliersData - inliers data to be reused on each iteration
- computeSortedQualityIndices
  
  private static int[] computeSortedQualityIndices(double[] qualityScores) throws com.irurueta.sorting.SortingException
  
  Obtains indices of samples corresponding to samples ordered in descending quality scores.
  
  Parameters:
  
  qualityScores - quality scores associated to each sample to be used to obtain indices to sort samples in descending order of quality values.
  
  Returns:
  
  indices to sort samples in descending order of quality values.
  
  Throws:
  
  com.irurueta.sorting.SortingException - if sorting fails.
- reverse
  
  private static void reverse(int[] array)
  
  Reverses provided array.
  
  Parameters:
  
  array - array to be reversed.
- computeIterations
  
  private static int computeIterations(double probInlier, int subsetSize, double confidence)
  
  Computes number of required iterations to achieve required confidence with current probability of inlier and sample subset size.
  
  Parameters:
  
  probInlier - probability of inlier.
  
  subsetSize - sample subset size.
  
  confidence - required confidence of result.
  
  Returns:
  
  number of required iterations.
- imin
  
  private static int imin(int subsetSize, int sampleSize, double beta)
  
  Non randomness states that i-m (where i is the cardinal of the set of inliers for a wrong model) follows the binomial distribution B(n,beta). For n big enough, B(n,beta) approximates to normal distribution N(mu, sigma^2) by the central limit theorem, with mu = n*beta and sigma = sqrt(n*beta*(1 - beta)). Psi, the probability that In_star out of n_star data points are by chance inliers to an arbitrary incorrect model, is set to 0.05 (5%, as in the original paper), and you must change the Chi2 value if you chose a different value for psi.
  
  Parameters:
  
  subsetSize - sample subset size.
  
  sampleSize - total number of samples.
  
  beta - beta value.
  
  Returns:
  
  i-m.
- keepInliersData
  
  private void keepInliersData(PROMedSRobustEstimator.PROMedSInliersData inliersData, int totalSamples)
  
  Keeps inliers data stored and initializes a new one with proper initial values.
  
  Parameters:
  
  inliersData - inliers data to be stored.
  
  totalSamples - total number of samples.

Class PROMedSRobustEstimator<T>

Nested Class Summary

Field Summary

Fields inherited from class com.irurueta.numerical.robust.RobustEstimator

Constructor Summary

Method Summary

Methods inherited from class com.irurueta.numerical.robust.RobustEstimator

Methods inherited from class java.lang.Object

Field Details

DEFAULT_CONFIDENCE

DEFAULT_MAX_ITERATIONS

MIN_CONFIDENCE

MAX_CONFIDENCE

MIN_ITERATIONS

DEFAULT_STOP_THRESHOLD_ENABLED

DEFAULT_MAX_OUTLIERS_PROPORTION

MIN_MAX_OUTLIERS_PROPORTION

MAX_MAX_OUTLIERS_PROPORTION

DEFAULT_ETA0

MIN_ETA0

MAX_ETA0

DEFAULT_BETA

MIN_BETA

MAX_BETA

DEFAULT_INLIER_FACTOR

MIN_INLER_FACTOR

DEFAULT_USE_INLIER_THRESHOLD

STD_CONSTANT

CHI_SQUARED

confidence

maxIterations

maxOutliersProportion

eta0

beta

subsetSelector

iters

bestResult

bestInliersData

stopThresholdEnabled

inlierFactor

useInlierThresholds

Constructor Details

PROMedSRobustEstimator

PROMedSRobustEstimator

Method Details

getConfidence

setConfidence

getMaxIterations

setMaxIterations

isStopThresholdEnabled

setStopThresholdEnabled

getInlierFactor

setInlierFactor

isUseInlierThresholds

setUseInlierThresholds

getMaxOutliersProportion

setMaxOutliersProportion

getEta0

setEta0

getBeta

setBeta

getNIters

getBestResult

getBestInliersData

isReady

estimate

getInliersData

getMethod

transformIndices

computeInliers

computeSortedQualityIndices

reverse

computeIterations

imin

keepInliersData