/*
    * Copyright (C) 2020 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.navigation.inertial.calibration.accelerometer;
  
  import com.irurueta.navigation.LockedException;
  import com.irurueta.navigation.NotReadyException;
  import com.irurueta.navigation.inertial.calibration.CalibrationException;
  import com.irurueta.navigation.inertial.calibration.StandardDeviationFrameBodyKinematics;
  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;
  
  /**
   * Robustly estimates accelerometer biases, cross couplings and scaling factors
   * using an LMedS algorithm to discard outliers.
   * <p>
   * To use this calibrator at least 4 measurements at different known frames must
   * be provided. In other words, accelerometer samples must be obtained at 4
   * different positions, orientations and velocities (although typically velocities are
   * always zero).
   * <p>
   * Measured specific force is assumed to follow the model shown below:
   * <pre>
   *     fmeas = ba + (I + Ma) * ftrue + w
   * </pre>
   * Where:
   * - fmeas is the measured specific force. This is a 3x1 vector.
   * - ba is accelerometer bias. Ideally, on a perfect accelerometer, this should be a
   * 3x1 zero vector.
   * - I is the 3x3 identity matrix.
   * - Ma is the 3x3 matrix containing cross-couplings and scaling factors. Ideally, on
   * a perfect accelerometer, this should be a 3x3 zero matrix.
   * - ftrue is ground-truth specific force.
   * - w is measurement noise.
   */
  public class LMedSRobustKnownFrameAccelerometerCalibrator extends RobustKnownFrameAccelerometerCalibrator {
  
      /**
       * Default value to be used for stop threshold. Stop threshold can be used to
       * avoid keeping the algorithm unnecessarily 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-8;
  
      /**
       * 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 = DEFAULT_STOP_THRESHOLD;
  
      /**
       * Constructor.
       */
     public LMedSRobustKnownFrameAccelerometerCalibrator() {
     }
 
     /**
      * Constructor.
      *
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      */
     public LMedSRobustKnownFrameAccelerometerCalibrator(
             final RobustKnownFrameAccelerometerCalibratorListener listener) {
         super(listener);
     }
 
     /**
      * Constructor.
      *
      * @param measurements list of body kinematics measurements with standard
      *                     deviations taken at different frames (positions, orientations
      *                     and velocities).
      */
     public LMedSRobustKnownFrameAccelerometerCalibrator(
             final List<StandardDeviationFrameBodyKinematics> measurements) {
         super(measurements);
     }
 
     /**
      * Constructor.
      *
      * @param measurements list of body kinematics measurements with standard
      *                     deviations taken at different frames (positions, orientations
      *                     and velocities).
      * @param listener     listener to handle events raised by this calibrator.
      */
     public LMedSRobustKnownFrameAccelerometerCalibrator(
             final List<StandardDeviationFrameBodyKinematics> measurements,
             final RobustKnownFrameAccelerometerCalibratorListener listener) {
         super(measurements, listener);
     }
 
     /**
      * Constructor.
      *
      * @param commonAxisUsed indicates whether z-axis is assumed to be common for
      *                       accelerometer and gyroscope.
      */
     public LMedSRobustKnownFrameAccelerometerCalibrator(final boolean commonAxisUsed) {
         super(commonAxisUsed);
     }
 
     /**
      * Constructor.
      *
      * @param commonAxisUsed indicates whether z-axis is assumed to be common for
      *                       accelerometer and gyroscope.
      * @param listener       listener to handle events raised by this calibrator.
      */
     public LMedSRobustKnownFrameAccelerometerCalibrator(
             final boolean commonAxisUsed, final RobustKnownFrameAccelerometerCalibratorListener listener) {
         super(commonAxisUsed, listener);
     }
 
     /**
      * Constructor.
      *
      * @param measurements   list of body kinematics measurements with standard
      *                       deviations taken at different frames (positions, orientations
      *                       and velocities).
      * @param commonAxisUsed indicates whether z-axis is assumed to be common for
      *                       accelerometer and gyroscope.
      */
     public LMedSRobustKnownFrameAccelerometerCalibrator(
             final List<StandardDeviationFrameBodyKinematics> measurements, final boolean commonAxisUsed) {
         super(measurements, commonAxisUsed);
     }
 
     /**
      * Constructor.
      *
      * @param measurements   list of body kinematics measurements with standard
      *                       deviations taken at different frames (positions, orientations
      *                       and velocities).
      * @param commonAxisUsed indicates whether z-axis is assumed to be common for
      *                       accelerometer and gyroscope.
      * @param listener       listener to handle events raised by this calibrator.
      */
     public LMedSRobustKnownFrameAccelerometerCalibrator(
             final List<StandardDeviationFrameBodyKinematics> measurements, final boolean commonAxisUsed,
             final RobustKnownFrameAccelerometerCalibratorListener listener) {
         super(measurements, commonAxisUsed, listener);
     }
 
     /**
      * 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 to iterate
      * 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 to iterate
      * 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 calibrator is currently running.
      */
     public void setStopThreshold(final double stopThreshold) throws LockedException {
         if (running) {
             throw new LockedException();
         }
         if (stopThreshold <= MIN_STOP_THRESHOLD) {
             throw new IllegalArgumentException();
         }
 
         this.stopThreshold = stopThreshold;
     }
 
     /**
      * Estimates accelerometer calibration parameters containing bias, scale factors
      * and cross-coupling errors.
      *
      * @throws LockedException      if calibrator is currently running.
      * @throws NotReadyException    if calibrator is not ready.
      * @throws CalibrationException if estimation fails for numerical reasons.
      */
     @SuppressWarnings("DuplicatedCode")
     @Override
     public void calibrate() throws LockedException, NotReadyException, CalibrationException {
         if (running) {
             throw new LockedException();
         }
         if (!isReady()) {
             throw new NotReadyException();
         }
 
         final var innerEstimator = new LMedSRobustEstimator<>(new LMedSRobustEstimatorListener<PreliminaryResult>() {
             @Override
             public int getTotalSamples() {
                 return measurements.size();
             }
 
             @Override
             public int getSubsetSize() {
                 return preliminarySubsetSize;
             }
 
             @Override
             public void estimatePreliminarSolutions(
                     final int[] samplesIndices, final List<PreliminaryResult> solutions) {
                 computePreliminarySolutions(samplesIndices, solutions);
             }
 
             @Override
             public double computeResidual(final PreliminaryResult currentEstimation, final int i) {
                 return computeError(measurements.get(i), currentEstimation);
             }
 
             @Override
             public boolean isReady() {
                 return LMedSRobustKnownFrameAccelerometerCalibrator.super.isReady();
             }
 
             @Override
             public void onEstimateStart(final RobustEstimator<PreliminaryResult> estimator) {
                 // no action needed
             }
 
             @Override
             public void onEstimateEnd(final RobustEstimator<PreliminaryResult> estimator) {
                 // no action needed
             }
 
             @Override
             public void onEstimateNextIteration(
                     final RobustEstimator<PreliminaryResult> estimator, final int iteration) {
                 if (listener != null) {
                     listener.onCalibrateNextIteration(
                             LMedSRobustKnownFrameAccelerometerCalibrator.this, iteration);
                 }
             }
 
             @Override
             public void onEstimateProgressChange(
                     final RobustEstimator<PreliminaryResult> estimator, final float progress) {
                 if (listener != null) {
                     listener.onCalibrateProgressChange(
                             LMedSRobustKnownFrameAccelerometerCalibrator.this, progress);
                 }
             }
         });
 
         try {
             running = true;
 
             if (listener != null) {
                 listener.onCalibrateStart(this);
             }
 
             inliersData = null;
             innerEstimator.setConfidence(confidence);
             innerEstimator.setMaxIterations(maxIterations);
             innerEstimator.setProgressDelta(progressDelta);
             innerEstimator.setStopThreshold(stopThreshold);
             final var preliminaryResult = innerEstimator.estimate();
             inliersData = innerEstimator.getInliersData();
 
             attemptRefine(preliminaryResult);
 
             if (listener != null) {
                 listener.onCalibrateEnd(this);
             }
 
         } catch (final com.irurueta.numerical.LockedException e) {
             throw new LockedException(e);
         } catch (final com.irurueta.numerical.NotReadyException e) {
             throw new NotReadyException(e);
         } catch (final RobustEstimatorException e) {
             throw new CalibrationException(e);
         } finally {
             running = false;
         }
     }
 
     /**
      * Returns method being used for robust estimation.
      *
      * @return method being used for robust estimation.
      */
     @Override
     public RobustEstimatorMethod getMethod() {
         return RobustEstimatorMethod.LMEDS;
     }
 
     /**
      * Indicates whether this calibrator requires quality scores for each
      * measurement or not.
      *
      * @return true if quality scores are required, false otherwise.
      */
     @Override
     public boolean isQualityScoresRequired() {
         return false;
     }
 }