/*
    * Copyright (C) 2015 Alberto Irurueta Carro (alberto@irurueta.com)
    *
    * Licensed under the Apache License, Version 2.0 (the "License");
    * you may not use this file except in compliance with the License.
    * You may obtain a copy of the License at
    *
    *         http://www.apache.org/licenses/LICENSE-2.0
    *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  package com.irurueta.geometry.estimators;
  
  import com.irurueta.algebra.Matrix;
  import com.irurueta.geometry.CoordinatesType;
  import com.irurueta.geometry.HomogeneousPoint2D;
  import com.irurueta.geometry.InhomogeneousPoint2D;
  import com.irurueta.geometry.Line2D;
  import com.irurueta.geometry.Point2D;
  import com.irurueta.geometry.refiners.HomogeneousPoint2DRefiner;
  import com.irurueta.geometry.refiners.InhomogeneousPoint2DRefiner;
  import com.irurueta.geometry.refiners.Point2DRefiner;
  import com.irurueta.numerical.robust.InliersData;
  import com.irurueta.numerical.robust.RobustEstimatorException;
  import com.irurueta.numerical.robust.RobustEstimatorMethod;
  
  import java.util.List;
  
  /**
   * This is an abstract class for algorithms to robustly find the best 3D point
   * that intersects in a collection of 2D lines.
   * Implementations of this class should be able to detect and discard outliers
   * in order to find the best solution.
   */
  @SuppressWarnings("DuplicatedCode")
  public abstract class Point2DRobustEstimator {
      /**
       * Minimum number of 2D lines required to estimate a point.
       */
      public static final int MINIMUM_SIZE = 2;
  
      /**
       * Default amount of progress variation before notifying a change in
       * estimation progress. By default, this is set to 5%.
       */
      public static final float DEFAULT_PROGRESS_DELTA = 0.05f;
  
      /**
       * Minimum allowed value for progress delta.
       */
      public static final float MIN_PROGRESS_DELTA = 0.0f;
  
      /**
       * Maximum allowed value for progress delta.
       */
      public static final float MAX_PROGRESS_DELTA = 1.0f;
  
      /**
       * 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.
       */
      public static final double DEFAULT_CONFIDENCE = 0.99;
  
      /**
       * Default maximum allowed number of iterations.
       */
      public static final int DEFAULT_MAX_ITERATIONS = 5000;
  
      /**
       * Minimum allowed confidence value.
       */
      public static final double MIN_CONFIDENCE = 0.0;
  
      /**
       * Maximum allowed confidence value.
       */
      public static final double MAX_CONFIDENCE = 1.0;
  
      /**
       * Minimum allowed number of iterations.
       */
      public static final int MIN_ITERATIONS = 1;
  
      /**
       * Default robust estimator method when none is provided.
       */
      public static final RobustEstimatorMethod DEFAULT_ROBUST_METHOD = RobustEstimatorMethod.PROMEDS;
  
      /**
       * Indicates that result is refined by default using Levenberg-Marquardt
       * fitting algorithm over found inliers.
       */
      public static final boolean DEFAULT_REFINE_RESULT = true;
  
     /**
      * Indicates that covariance is not kept by default after refining result.
      */
     public static final boolean DEFAULT_KEEP_COVARIANCE = false;
 
     /**
      * Listener to be notified of events such as when estimation starts, ends
      * or its progress significantly changes.
      */
     protected Point2DRobustEstimatorListener listener;
 
     /**
      * Indicates if this estimator is locked because an estimation is being
      * computed.
      */
     protected volatile boolean locked;
 
     /**
      * Amount of progress variation before notifying a progress change during
      * estimation.
      */
     protected float progressDelta;
 
     /**
      * Amount of confidence expressed as a value between 0.0 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.
      */
     protected double confidence;
 
     /**
      * 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.
      */
     protected int maxIterations;
 
     /**
      * List of lines to be used to estimate a 2D point. Provided list must have
      * a size greater or equal than MINIMUM_SIZE.
      */
     protected List<Line2D> lines;
 
     /**
      * Data related to inliers found after estimation.
      */
     protected InliersData inliersData;
 
     /**
      * Indicates whether result must be refined using Levenberg-Marquardt
      * fitting algorithm over found inliers.
      * If true, inliers will be computed and kept in any implementation
      * regardless of the settings.
      */
     protected boolean refineResult;
 
     /**
      * Coordinates type to use for refinement. When using inhomogeneous
      * coordinates a 3x3 covariance matrix is estimated. When using homogeneous
      * coordinates a 4x4 covariance matrix is estimated.
      */
     private CoordinatesType refinementCoordinatesType = CoordinatesType.INHOMOGENEOUS_COORDINATES;
 
     /**
      * Indicates whether covariance must be kept after refining result.
      * This setting is only taken into account if result is refined.
      */
     private boolean keepCovariance;
 
     /**
      * Estimated covariance of estimated 2D point.
      * This is only available when result has been refined and covariance is
      * kept.
      */
     private Matrix covariance;
 
     /**
      * Constructor.
      */
     protected Point2DRobustEstimator() {
         progressDelta = DEFAULT_PROGRESS_DELTA;
         confidence = DEFAULT_CONFIDENCE;
         maxIterations = DEFAULT_MAX_ITERATIONS;
         refineResult = DEFAULT_REFINE_RESULT;
         keepCovariance = DEFAULT_KEEP_COVARIANCE;
     }
 
     /**
      * Constructor.
      *
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      */
     protected Point2DRobustEstimator(final Point2DRobustEstimatorListener listener) {
         this.listener = listener;
         progressDelta = DEFAULT_PROGRESS_DELTA;
         confidence = DEFAULT_CONFIDENCE;
         maxIterations = DEFAULT_MAX_ITERATIONS;
         refineResult = DEFAULT_REFINE_RESULT;
         keepCovariance = DEFAULT_KEEP_COVARIANCE;
     }
 
     /**
      * Constructor with lines.
      *
      * @param lines 2D lines to estimate a 2D point.
      * @throws IllegalArgumentException if provided list of lines don't have
      *                                  a size greater or equal than MINIMUM_SIZE.
      */
     protected Point2DRobustEstimator(final List<Line2D> lines) {
         progressDelta = DEFAULT_PROGRESS_DELTA;
         confidence = DEFAULT_CONFIDENCE;
         maxIterations = DEFAULT_MAX_ITERATIONS;
         internalSetLines(lines);
         refineResult = DEFAULT_REFINE_RESULT;
         keepCovariance = DEFAULT_KEEP_COVARIANCE;
     }
 
     /**
      * Constructor.
      *
      * @param lines    2D lines to estimate a 2D point.
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      * @throws IllegalArgumentException if provided list of lines don't have
      *                                  a size greater or equal than MINIMUM_SIZE.
      */
     protected Point2DRobustEstimator(final Point2DRobustEstimatorListener listener, final List<Line2D> lines) {
         this.listener = listener;
         progressDelta = DEFAULT_PROGRESS_DELTA;
         confidence = DEFAULT_CONFIDENCE;
         maxIterations = DEFAULT_MAX_ITERATIONS;
         internalSetLines(lines);
         refineResult = DEFAULT_REFINE_RESULT;
         keepCovariance = DEFAULT_KEEP_COVARIANCE;
     }
 
 
     /**
      * Returns reference to listener to be notified of events such as when
      * estimation starts, ends or its progress significantly changes.
      *
      * @return listener to be notified of events.
      */
     public Point2DRobustEstimatorListener getListener() {
         return listener;
     }
 
     /**
      * Sets listener to be notified of events such as when estimation starts,
      * ends or its progress significantly changes.
      *
      * @param listener listener to be notified of events.
      * @throws LockedException if robust estimator is locked.
      */
     public void setListener(final Point2DRobustEstimatorListener listener) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         this.listener = listener;
     }
 
     /**
      * Indicates whether listener has been provided and is available for
      * retrieval.
      *
      * @return true if available, false otherwise.
      */
     public boolean isListenerAvailable() {
         return listener != null;
     }
 
     /**
      * Indicates if this instance is locked because estimation is being computed.
      *
      * @return true if locked, false otherwise.
      */
     public boolean isLocked() {
         return locked;
     }
 
     /**
      * Returns amount of progress variation before notifying a progress change
      * during estimation.
      *
      * @return amount of progress variation before notifying a progress change
      * during estimation.
      */
     public float getProgressDelta() {
         return progressDelta;
     }
 
     /**
      * Sets amount of progress variation before notifying a progress change
      * during estimation.
      *
      * @param progressDelta amount of progress variation before notifying a
      *                      progress change during estimation.
      * @throws IllegalArgumentException if progress delta is less than zero or
      *                                  greater than 1.
      * @throws LockedException          if this estimator is locked because an estimation
      *                                  is being computed.
      */
     public void setProgressDelta(final float progressDelta) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (progressDelta < MIN_PROGRESS_DELTA || progressDelta > MAX_PROGRESS_DELTA) {
             throw new IllegalArgumentException();
         }
         this.progressDelta = progressDelta;
     }
 
     /**
      * Returns amount of confidence expressed as a value between 0.0 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.
      *
      * @return amount of confidence as a value between 0.0 and 1.0.
      */
     public double getConfidence() {
         return confidence;
     }
 
     /**
      * Sets amount of confidence expressed as a value between 0.0 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.
      *
      * @param 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.
      * @throws LockedException          if this estimator is locked because an estimator
      *                                  is being computed.
      */
     public void setConfidence(final double confidence) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (confidence < MIN_CONFIDENCE || confidence > MAX_CONFIDENCE) {
             throw new IllegalArgumentException();
         }
         this.confidence = confidence;
     }
 
     /**
      * Returns maximum allowed number of iterations. If maximum allowed number
      * of iterations is achieved without converging to a result when calling
      * estimate(), a RobustEstimatorException will be raised.
      *
      * @return maximum allowed number of iterations.
      */
     public int getMaxIterations() {
         return maxIterations;
     }
 
     /**
      * 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.
      *
      * @param maxIterations maximum allowed number of iterations to be set.
      * @throws IllegalArgumentException if provided value is less than 1.
      * @throws LockedException          if this estimator is locked because an estimation
      *                                  is being computed.
      */
     public void setMaxIterations(final int maxIterations) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (maxIterations < MIN_ITERATIONS) {
             throw new IllegalArgumentException();
         }
         this.maxIterations = maxIterations;
     }
 
     /**
      * Gets data related to inliers found after estimation.
      *
      * @return data related to inliers found after estimation.
      */
     public InliersData getInliersData() {
         return inliersData;
     }
 
     /**
      * Indicates whether result must be refined using Levenberg-Marquardt
      * fitting algorithm over found inliers.
      * If true, inliers will be computed and kept in any implementation
      * regardless of the settings.
      *
      * @return true to refine result, false to simply use result found by
      * robust estimator without further refining.
      */
     public boolean isResultRefined() {
         return refineResult;
     }
 
     /**
      * Specifies whether result must be refined using Levenberg-Marquardt
      * fitting algorithm over found inliers.
      *
      * @param refineResult true to refine result, false to simply use result
      *                     found by robust estimator without further refining.
      * @throws LockedException if estimator is locked.
      */
     public void setResultRefined(final boolean refineResult) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         this.refineResult = refineResult;
     }
 
     /**
      * Gets coordinates type to use for refinement. When using inhomogeneous
      * coordinates a 3x3 covariance matrix is estimated. When using homogeneous
      * coordinates a 4x4 covariance matrix is estimated.
      *
      * @return coordinates type to use for refinement.
      */
     public CoordinatesType getRefinementCoordinatesType() {
         return refinementCoordinatesType;
     }
 
     /**
      * Sets coordinates type to use for refinement. When using inhomogeneous
      * coordinates a 3x3 covariance matrix is estimated. When using homogeneous
      * coordinates a 4x4 covariance matrix is estimated.
      *
      * @param refinementCoordinatesType coordinates type to use for refinement.
      * @throws LockedException if estimator is locked.
      */
     public void setRefinementCoordinatesType(final CoordinatesType refinementCoordinatesType) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         this.refinementCoordinatesType = refinementCoordinatesType;
     }
 
     /**
      * Indicates whether covariance must be kept after refining result.
      * This setting is only taken into account if result is refined.
      *
      * @return true if covariance must be kept after refining result, false
      * otherwise.
      */
     public boolean isCovarianceKept() {
         return keepCovariance;
     }
 
     /**
      * Specifies whether covariance must be kept after refining result.
      * This setting is only taken into account if result is refined.
      *
      * @param keepCovariance true if covariance must be kept after refining
      *                       result, false otherwise.
      * @throws LockedException if estimator is locked.
      */
     public void setCovarianceKept(final boolean keepCovariance) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         this.keepCovariance = keepCovariance;
     }
 
     /**
      * Returns list of lines to be used to estimate a 2D point.
      * Provided list must have a size greater or equal than MINIMUM_SIZE
      *
      * @return list of lines to be used to estimate a 2D point.
      */
     public List<Line2D> getLines() {
         return lines;
     }
 
     /**
      * Sets list of lines to be used to estimate a 2D point.
      * Provided list must have a size greater or equal than MINIMUM_SIZE.
      *
      * @param lines list of lines to be used to estimate a 2D point.
      * @throws IllegalArgumentException if provided list of lines don't have
      *                                  a size greater or equal than MINIMUM_SIZE.
      * @throws LockedException          if estimator is locked because a computation is
      *                                  already in progress.
      */
     public void setLines(final List<Line2D> lines) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         internalSetLines(lines);
     }
 
     /**
      * Indicates if estimator is ready to start the 2D point estimation.
      * This is true when a minimum if MINIMUM_SIZE lines are available.
      *
      * @return true if estimator is ready, false otherwise.
      */
     public boolean isReady() {
         return lines != null && lines.size() >= MINIMUM_SIZE;
     }
 
     /**
      * Returns quality scores corresponding to each line.
      * The larger the score value the better the quality of the line measure.
      * This implementation always returns null.
      * Subclasses using quality scores must implement proper behaviour.
      *
      * @return quality scores corresponding to each point.
      */
     public double[] getQualityScores() {
         return null;
     }
 
     /**
      * Sets quality scores corresponding to each line.
      * The larger the score value the better the quality of the line measure.
      * This implementation makes no action.
      * Subclasses using quality scores must implement proper behaviour.
      *
      * @param qualityScores quality scores corresponding to each sampled line.
      * @throws LockedException          if robust estimator is locked because an
      *                                  estimation is already in progress.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 2 samples).
      */
     public void setQualityScores(final double[] qualityScores) throws LockedException {
     }
 
     /**
      * Gets estimated covariance of estimated 3D point if available.
      * This is only available when result has been refined and covariance is
      * kept.
      *
      * @return estimated covariance or null.
      */
     public Matrix getCovariance() {
         return covariance;
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided robust estimator method.
      *
      * @param method method of a robust estimator algorithm to estimate the best
      *               2D point.
      * @return an instance of a 2D point robust estimator.
      */
     public static Point2DRobustEstimator create(final RobustEstimatorMethod method) {
         return switch (method) {
             case LMEDS -> new LMedSPoint2DRobustEstimator();
             case MSAC -> new MSACPoint2DRobustEstimator();
             case PROSAC -> new PROSACPoint2DRobustEstimator();
             case PROMEDS -> new PROMedSPoint2DRobustEstimator();
             default -> new RANSACPoint2DRobustEstimator();
         };
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided lines and robust estimator method.
      *
      * @param lines  2D lines to estimate a 2D point.
      * @param method method of a robust estimator algorithm to estimate the best
      *               2D point.
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided list of lines don't have a
      *                                  size greater or equal than MINIMUM_SIZE.
      */
     public static Point2DRobustEstimator create(final List<Line2D> lines, final RobustEstimatorMethod method) {
         return switch (method) {
             case LMEDS -> new LMedSPoint2DRobustEstimator(lines);
             case MSAC -> new MSACPoint2DRobustEstimator(lines);
             case PROSAC -> new PROSACPoint2DRobustEstimator(lines);
             case PROMEDS -> new PROMedSPoint2DRobustEstimator(lines);
             default -> new RANSACPoint2DRobustEstimator(lines);
         };
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided listener.
      *
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      * @param method   method of a robust estimator algorithm to estimate the best
      *                 2D point.
      * @return an instance of a 2D point robust estimator.
      */
     public static Point2DRobustEstimator create(
             final Point2DRobustEstimatorListener listener, final RobustEstimatorMethod method) {
         return switch (method) {
             case LMEDS -> new LMedSPoint2DRobustEstimator(listener);
             case MSAC -> new MSACPoint2DRobustEstimator(listener);
             case PROSAC -> new PROSACPoint2DRobustEstimator(listener);
             case PROMEDS -> new PROMedSPoint2DRobustEstimator(listener);
             default -> new RANSACPoint2DRobustEstimator(listener);
         };
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided listener and lines.
      *
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      * @param lines    2D lines to estimate a 2D point.
      * @param method   method of a robust estimator algorithm to estimate the best
      *                 2D point.
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided list of lines don't have a
      *                                  size greater or equal than MINIMUM_SIZE.
      */
     public static Point2DRobustEstimator create(
             final Point2DRobustEstimatorListener listener, final List<Line2D> lines,
             final RobustEstimatorMethod method) {
         return switch (method) {
             case LMEDS -> new LMedSPoint2DRobustEstimator(listener, lines);
             case MSAC -> new MSACPoint2DRobustEstimator(listener, lines);
             case PROSAC -> new PROSACPoint2DRobustEstimator(listener, lines);
             case PROMEDS -> new PROMedSPoint2DRobustEstimator(listener, lines);
             default -> new RANSACPoint2DRobustEstimator(listener, lines);
         };
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided robust estimator method.
      *
      * @param qualityScores quality scores corresponding to each provided line.
      * @param method        method of a robust estimator algorithm to estimate the best
      *                      2D point.
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 2 lines).
      */
     public static Point2DRobustEstimator create(final double[] qualityScores, final RobustEstimatorMethod method) {
         return switch (method) {
             case LMEDS -> new LMedSPoint2DRobustEstimator();
             case MSAC -> new MSACPoint2DRobustEstimator();
             case PROSAC -> new PROSACPoint2DRobustEstimator(qualityScores);
             case PROMEDS -> new PROMedSPoint2DRobustEstimator(qualityScores);
             default -> new RANSACPoint2DRobustEstimator();
         };
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided lines and robust estimator method.
      *
      * @param lines         2D lines to estimate a 2D point.
      * @param qualityScores quality scores corresponding to each provided line.
      * @param method        method of a robust estimator algorithm to estimate the best
      *                      2D point.
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided list of lines don't have
      *                                  the same size as the list of provided quality scores, or it their size
      *                                  is not greater or equal than MINIMUM_SIZE.
      */
     public static Point2DRobustEstimator create(
             final List<Line2D> lines, final double[] qualityScores, final RobustEstimatorMethod method) {
         return switch (method) {
             case LMEDS -> new LMedSPoint2DRobustEstimator(lines);
             case MSAC -> new MSACPoint2DRobustEstimator(lines);
             case PROSAC -> new PROSACPoint2DRobustEstimator(lines, qualityScores);
             case PROMEDS -> new PROMedSPoint2DRobustEstimator(lines, qualityScores);
             default -> new RANSACPoint2DRobustEstimator(lines);
         };
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided listener.
      *
      * @param listener      listener to be notified of events such as when estimation
      *                      starts, ends or its progress significantly changes.
      * @param qualityScores quality scores corresponding to each provided line.
      * @param method        method of a robust estimator algorithm to estimate the best
      *                      2D point.
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 2 lines).
      */
     public static Point2DRobustEstimator create(
             final Point2DRobustEstimatorListener listener, final double[] qualityScores,
             final RobustEstimatorMethod method) {
         return switch (method) {
             case LMEDS -> new LMedSPoint2DRobustEstimator(listener);
             case MSAC -> new MSACPoint2DRobustEstimator(listener);
             case PROSAC -> new PROSACPoint2DRobustEstimator(listener, qualityScores);
             case PROMEDS -> new PROMedSPoint2DRobustEstimator(listener, qualityScores);
             default -> new RANSACPoint2DRobustEstimator(listener);
         };
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided listener and lines.
      *
      * @param listener      listener to be notified of events such as when estimation
      *                      starts, ends or its progress significantly changes.
      * @param lines         2D lines to estimate a 2D point.
      * @param qualityScores quality scores corresponding to each provided point.
      * @param method        method of a robust estimator algorithm to estimate the best
      *                      2D point.
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided list of lines don't have
      *                                  the same size as the list of provided quality scores, or it their size
      *                                  is not greater or equal than MINIMUM_SIZE.
      */
     public static Point2DRobustEstimator create(
             final Point2DRobustEstimatorListener listener, final List<Line2D> lines, final double[] qualityScores,
             final RobustEstimatorMethod method) {
         return switch (method) {
             case LMEDS -> new LMedSPoint2DRobustEstimator(listener, lines);
             case MSAC -> new MSACPoint2DRobustEstimator(listener, lines);
             case PROSAC -> new PROSACPoint2DRobustEstimator(listener, lines, qualityScores);
             case PROMEDS -> new PROMedSPoint2DRobustEstimator(listener, lines, qualityScores);
             default -> new RANSACPoint2DRobustEstimator(listener, lines);
         };
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * default robust estimator method.
      *
      * @return an instance of a 2D point robust estimator.
      */
     public static Point2DRobustEstimator create() {
         return create(DEFAULT_ROBUST_METHOD);
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided lines and default robust estimator method.
      *
      * @param lines 2D lines to estimate a 2D point.
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided list of lines don't have a
      *                                  size greater or equal than MINIMUM_SIZE.
      */
     public static Point2DRobustEstimator create(final List<Line2D> lines) {
         return create(lines, DEFAULT_ROBUST_METHOD);
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided listener and default robust estimator method.
      *
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      * @return an instance of a 2D point robust estimator.
      */
     public static Point2DRobustEstimator create(final Point2DRobustEstimatorListener listener) {
         return create(listener, DEFAULT_ROBUST_METHOD);
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided listener and lines and default robust estimator method.
      *
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      * @param lines    2D lines to estimate a point.
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided list of lines don't have a
      *                                  size greater or equal than MINIMUM_SIZE.
      */
     public static Point2DRobustEstimator create(
             final Point2DRobustEstimatorListener listener, final List<Line2D> lines) {
         return create(listener, lines, DEFAULT_ROBUST_METHOD);
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * default robust estimator method.
      *
      * @param qualityScores quality scores corresponding to each provided line
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 2 lines).
      */
     public static Point2DRobustEstimator create(final double[] qualityScores) {
         return create(qualityScores, DEFAULT_ROBUST_METHOD);
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided lines and default estimator method.
      *
      * @param lines         2D lines to estimate a 2D point.
      * @param qualityScores quality scores corresponding to each provided line.
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided list of lines don't have
      *                                  the same size as the list of provided quality scores, or if their size
      *                                  is not greater or equal than MINIMUM_SIZE.
      */
     public static Point2DRobustEstimator create(final List<Line2D> lines, final double[] qualityScores) {
         return create(lines, qualityScores, DEFAULT_ROBUST_METHOD);
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided listener and default estimator method.
      *
      * @param listener      listener to be notified of events such as when estimation
      *                      starts, ends or its progress significantly changes.
      * @param qualityScores quality scores corresponding to each provided line
      * @return an instance of a circle robust estimator.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 2 lines).
      */
     public static Point2DRobustEstimator create(
             final Point2DRobustEstimatorListener listener, final double[] qualityScores) {
         return create(listener, qualityScores, DEFAULT_ROBUST_METHOD);
     }
 
     /**
      * Creates a 2D point robust estimator based on 2D line samples and using
      * provided listener and lines and default estimator method.
      *
      * @param listener      listener to be notified of events such as when estimation
      *                      starts, ends or its progress significantly changes.
      * @param lines         2D lines to estimate a 2D point.
      * @param qualityScores quality scores corresponding to each provided line.
      * @return an instance of a 2D point robust estimator.
      * @throws IllegalArgumentException if provided list of lines don't have
      *                                  the same size as the list of provided quality scores, or if their size
      *                                  is not greater or equal than MINIMUM_SIZE.
      */
     public static Point2DRobustEstimator create(
             final Point2DRobustEstimatorListener listener, final List<Line2D> lines, final double[] qualityScores) {
         return create(listener, lines, qualityScores, DEFAULT_ROBUST_METHOD);
     }
 
     /**
      * Estimates a 2D point using a robust estimator and the best set of 2D
      * lines that intersect into the estimated 2D point.
      *
      * @return a 2D point.
      * @throws LockedException          if robust estimator is locked because an
      *                                  estimation is already in progress.
      * @throws NotReadyException        if provided input data is not enough to start
      *                                  the estimation.
      * @throws RobustEstimatorException if estimation fails for any reason
      *                                  (i.e. numerical instability, no solution available, etc).
      */
     public abstract Point2D estimate() throws LockedException, NotReadyException, RobustEstimatorException;
 
     /**
      * Returns method being used for robust estimation.
      *
      * @return method being used for robust estimation.
      */
     public abstract RobustEstimatorMethod getMethod();
 
     /**
      * Computes the residual between a 2D point and a line.
      *
      * @param p    a 2D point.
      * @param line a 2D line.
      * @return residual.
      */
     protected double residual(final Point2D p, final Line2D line) {
         p.normalize();
         line.normalize();
 
         return Math.abs(line.signedDistance(p));
     }
 
     /**
      * Attempts to refine provided solution if refinement is requested.
      * This method returns a refined solution or the same provided solution
      * if refinement is not requested or has failed.
      * If refinement is enabled, and it is requested to keep covariance, this
      * method will also keep covariance of refined point.
      *
      * @param point point estimated by a robust estimator without refinement.
      * @return solution after refinement (if requested) or the provided non-refined
      * solution if not requested or if refinement failed.
      */
     protected Point2D attemptRefine(final Point2D point) {
         if (refineResult) {
             try {
                 final Point2DRefiner<? extends Point2D> refiner;
                 final Point2D result;
                 final boolean improved;
                 switch (refinementCoordinatesType) {
                     case HOMOGENEOUS_COORDINATES:
                         final HomogeneousPoint2D homP;
                         if (point.getType() == CoordinatesType.HOMOGENEOUS_COORDINATES) {
                             homP = (HomogeneousPoint2D) point;
                         } else {
                             homP = new HomogeneousPoint2D(point);
                         }
                         final var homRefiner = new HomogeneousPoint2DRefiner(homP, keepCovariance, getInliersData(),
                                 lines, getRefinementStandardDeviation());
                         refiner = homRefiner;
                         final var homResult = new HomogeneousPoint2D();
                         improved = homRefiner.refine(homResult);
                         result = homResult;
                         break;
                     case INHOMOGENEOUS_COORDINATES:
                     default:
                         InhomogeneousPoint2D inhomP;
                         if (point.getType() == CoordinatesType.INHOMOGENEOUS_COORDINATES) {
                             inhomP = (InhomogeneousPoint2D) point;
                         } else {
                             inhomP = new InhomogeneousPoint2D(point);
                         }
                         final var inhomRefiner = new InhomogeneousPoint2DRefiner(inhomP, keepCovariance,
                                 getInliersData(), lines, getRefinementStandardDeviation());
                         refiner = inhomRefiner;
                         final var inhomResult = new InhomogeneousPoint2D();
                         improved = inhomRefiner.refine(inhomResult);
                         result = inhomResult;
                         break;
                 }
 
                 if (keepCovariance) {
                     // keep covariance
                     covariance = refiner.getCovariance();
                 }
 
                 return improved ? result : point;
             } catch (final Exception e) {
                 // refinement failed, so we return input value
                 return point;
             }
         } else {
             return point;
         }
     }
 
     /**
      * Gets standard deviation used for Levenberg-Marquardt fitting during
      * refinement.
      * Returned value gives an indication of how much variance each residual
      * has.
      * Typically, this value is related to the threshold used on each robust
      * estimation, since residuals of found inliers are within the range of
      * such threshold.
      *
      * @return standard deviation used for refinement.
      */
     protected abstract double getRefinementStandardDeviation();
 
     /**
      * Internal method to set list of 2D lines to be used to estimate a 2D
      * point.
      * This method does not check whether estimator is locked or not
      *
      * @param lines list of lines to be used to estimate a 2D point
      * @throws IllegalArgumentException if provided list of lines doesn't have
      *                                  a size greater or equal than MINIMUM_SIZE.
      */
     private void internalSetLines(final List<Line2D> lines) {
         if (lines.size() < MINIMUM_SIZE) {
             throw new IllegalArgumentException();
         }
         this.lines = lines;
     }
 }