/*
    * 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.geometry.CoincidentLinesException;
  import com.irurueta.geometry.DualConic;
  import com.irurueta.geometry.Line2D;
  import com.irurueta.numerical.robust.LMedSRobustEstimator;
  import com.irurueta.numerical.robust.LMedSRobustEstimatorListener;
  import com.irurueta.numerical.robust.RobustEstimator;
  import com.irurueta.numerical.robust.RobustEstimatorException;
  import com.irurueta.numerical.robust.RobustEstimatorMethod;
  
  import java.util.List;
  
  /**
   * Finds the best dual conic for provided collection of 2D lines using LMedS
   * algorithm.
   */
  @SuppressWarnings("DuplicatedCode")
  public class LMedSDualConicRobustEstimator extends DualConicRobustEstimator {
      /**
       * Default value to be used for stop threshold. Stop threshold can be used
       * to keep the algorithm iterating in case that best estimated threshold
       * using median of residuals is not small enough. Once a solution is found
       * that generates a threshold below this value, the algorithm will stop.
       * The stop threshold can be used to prevent the LMedS algorithm iterating
       * too many times in cases where samples have a very similar accuracy.
       * For instance, in cases where proportion of outliers is very small (close
       * to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
       * iterate for a long time trying to find the best solution when indeed
       * there is no need to do that if a reasonable threshold has already been
       * reached.
       * Because of this behaviour the stop threshold can be set to a value much
       * lower than the one typically used in RANSAC, and yet the algorithm could
       * still produce even smaller thresholds in estimated results.
       */
      public static final double DEFAULT_STOP_THRESHOLD = 1e-9;
  
      /**
       * Minimum allowed stop threshold value.
       */
      public static final double MIN_STOP_THRESHOLD = 0.0;
  
      /**
       * Threshold to be used to keep the algorithm iterating in case that best
       * estimated threshold using median of residuals is not small enough. Once
       * a solution is found that generates a threshold below this value, the
       * algorithm will stop.
       * The stop threshold can be used to prevent the LMedS algorithm iterating
       * too many times in cases where samples have a very similar accuracy.
       * For instance, in cases where proportion of outliers is very small (close
       * to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
       * iterate for a long time trying to find the best solution when indeed
       * there is no need to do that if a reasonable threshold has already been
       * reached.
       * Because of this behaviour the stop threshold can be set to a value much
       * lower than the one typically used in RANSAC, and yet the algorithm could
       * still produce even smaller thresholds in estimated results.
       */
      private double stopThreshold;
  
      /**
       * Constructor.
       */
      public LMedSDualConicRobustEstimator() {
          super();
          stopThreshold = DEFAULT_STOP_THRESHOLD;
      }
  
      /**
       * Constructor with points.
       *
       * @param lines 2D lines to estimate a dual conic.
       * @throws IllegalArgumentException if provided list of lines don't have
       *                                  a size greater or equal than MINIMUM_SIZE.
       */
      public LMedSDualConicRobustEstimator(final List<Line2D> lines) {
          super(lines);
          stopThreshold = DEFAULT_STOP_THRESHOLD;
      }
  
      /**
       * Constructor.
       *
       * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      */
     public LMedSDualConicRobustEstimator(final DualConicRobustEstimatorListener listener) {
         super(listener);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Constructor.
      *
      * @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 dual conic.
      * @throws IllegalArgumentException if provided list of lines don't have a
      *                                  size greater or equal than MINIMUM_SIZE.
      */
     public LMedSDualConicRobustEstimator(
             final DualConicRobustEstimatorListener listener, final List<Line2D> lines) {
         super(listener, lines);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Returns threshold to be used to keep the algorithm iterating in case that
      * best estimated threshold using median of residuals is not small enough.
      * Once a solution is found that generates a threshold below this value, the
      * algorithm will stop.
      * The stop threshold can be used to prevent the LMedS algorithm iterating
      * too many times in cases where samples have a very similar accuracy.
      * For instance, in cases where proportion of outliers is very small (close
      * to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
      * iterate for a long time trying to find the best solution when indeed
      * there is no need to do that if a reasonable threshold has already been
      * reached.
      * Because of this behaviour the stop threshold can be set to a value much
      * lower than the one typically used in RANSAC, and yet the algorithm could
      * still produce even smaller thresholds in estimated results.
      *
      * @return stop threshold to stop the algorithm prematurely when a certain
      * accuracy has been reached.
      */
     public double getStopThreshold() {
         return 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.
      * Once a solution is found that generates a threshold below this value, the
      * algorithm will stop.
      * The stop threshold can be used to prevent the LMedS algorithm iterating
      * too many times in cases where samples have a very similar accuracy.
      * For instance, in cases where proportion of outliers is very small (close
      * to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
      * iterate for a long time trying to find the best solution when indeed
      * there is no need to do that if a reasonable threshold has already been
      * reached.
      * Because of this behaviour the stop threshold can be set to a value much
      * lower than the one typically used in RANSAC, and yet the algorithm could
      * still produce even smaller thresholds in estimated results.
      *
      * @param stopThreshold stop threshold to stop the algorithm prematurely
      *                      when a certain accuracy has been reached.
      * @throws IllegalArgumentException if provided value is zero or negative.
      * @throws LockedException          if robust estimator is locked because an
      *                                  estimation is already in progress.
      */
     public void setStopThreshold(final double stopThreshold) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (stopThreshold <= MIN_STOP_THRESHOLD) {
             throw new IllegalArgumentException();
         }
 
         this.stopThreshold = stopThreshold;
     }
 
     /**
      * Estimates a dual conic using a robust estimator and the best set of 2D
      * lines that fit into the locus of the estimated dual conic found using the
      * robust estimator.
      *
      * @return a dual conic.
      * @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).
      */
     @Override
     public DualConic estimate() throws LockedException, NotReadyException, RobustEstimatorException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (!isReady()) {
             throw new NotReadyException();
         }
 
         final var innerEstimator = new LMedSRobustEstimator<>(new LMedSRobustEstimatorListener<DualConic>() {
 
             @Override
             public int getTotalSamples() {
                 return lines.size();
             }
 
             @Override
             public int getSubsetSize() {
                 return DualConicRobustEstimator.MINIMUM_SIZE;
             }
 
             @Override
             public void estimatePreliminarSolutions(final int[] samplesIndices, final List<DualConic> solutions) {
                 final var line1 = lines.get(samplesIndices[0]);
                 final var line2 = lines.get(samplesIndices[1]);
                 final var line3 = lines.get(samplesIndices[2]);
                 final var line4 = lines.get(samplesIndices[3]);
                 final var line5 = lines.get(samplesIndices[4]);
 
                 try {
                     final var dualConic = new DualConic(line1, line2, line3, line4, line5);
                     solutions.add(dualConic);
                 } catch (final CoincidentLinesException e) {
                     // if points are coincident, no solution is added
                 }
             }
 
             @Override
             public double computeResidual(final DualConic currentEstimation, final int i) {
                 return residual(currentEstimation, lines.get(i));
             }
 
             @Override
             public boolean isReady() {
                 return LMedSDualConicRobustEstimator.this.isReady();
             }
 
             @Override
             public void onEstimateStart(final RobustEstimator<DualConic> estimator) {
                 if (listener != null) {
                     listener.onEstimateStart(LMedSDualConicRobustEstimator.this);
                 }
             }
 
             @Override
             public void onEstimateEnd(final RobustEstimator<DualConic> estimator) {
                 if (listener != null) {
                     listener.onEstimateEnd(LMedSDualConicRobustEstimator.this);
                 }
             }
 
             @Override
             public void onEstimateNextIteration(final RobustEstimator<DualConic> estimator, final int iteration) {
                 if (listener != null) {
                     listener.onEstimateNextIteration(LMedSDualConicRobustEstimator.this, iteration);
                 }
             }
 
             @Override
             public void onEstimateProgressChange(final RobustEstimator<DualConic> estimator, final float progress) {
                 if (listener != null) {
                     listener.onEstimateProgressChange(LMedSDualConicRobustEstimator.this, progress);
                 }
             }
         });
 
         try {
             locked = true;
             innerEstimator.setConfidence(confidence);
             innerEstimator.setMaxIterations(maxIterations);
             innerEstimator.setProgressDelta(progressDelta);
             innerEstimator.setStopThreshold(stopThreshold);
             return innerEstimator.estimate();
         } catch (final com.irurueta.numerical.LockedException e) {
             throw new LockedException(e);
         } catch (final com.irurueta.numerical.NotReadyException e) {
             throw new NotReadyException(e);
         } finally {
             locked = false;
         }
     }
 
     /**
      * Returns method being used for robust estimation.
      *
      * @return method being used for robust estimation.
      */
     @Override
     public RobustEstimatorMethod getMethod() {
         return RobustEstimatorMethod.LMEDS;
     }
 }