/*
    * Copyright (C) 2016 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.numerical.polynomials.estimators;
  
  import com.irurueta.numerical.LockedException;
  import com.irurueta.numerical.NotReadyException;
  import com.irurueta.numerical.polynomials.Polynomial;
  import com.irurueta.numerical.robust.PROMedSRobustEstimator;
  import com.irurueta.numerical.robust.PROMedSRobustEstimatorListener;
  import com.irurueta.numerical.robust.RobustEstimator;
  import com.irurueta.numerical.robust.RobustEstimatorException;
  import com.irurueta.numerical.robust.RobustEstimatorMethod;
  
  import java.util.ArrayList;
  import java.util.List;
  
  /**
   * Finds the best polynomial using PROMedS algorithm.
   */
  public class PROMedSPolynomialRobustEstimator extends PolynomialRobustEstimator {
  
      /**
       * 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-6;
  
      /**
       * 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;
  
      /**
       * Quality scores corresponding to each provided polynomial evaluation.
       * The larger the score value the better the quality of the sample.
       */
      private double[] qualityScores;
  
      /**
       * Constructor.
       */
      public PROMedSPolynomialRobustEstimator() {
          super();
          stopThreshold = DEFAULT_STOP_THRESHOLD;
      }
  
      /**
       * Constructor.
       *
       * @param degree degree of polynomial to be estimated.
       * @throws IllegalArgumentException if provided degree is less than 1.
       */
      public PROMedSPolynomialRobustEstimator(final int degree) {
          super(degree);
          stopThreshold = DEFAULT_STOP_THRESHOLD;
      }
 
     /**
      * Constructor.
      *
      * @param evaluations collection of polynomial evaluations.
      * @throws IllegalArgumentException if provided number of evaluations is
      *                                  less than the required minimum.
      */
     public PROMedSPolynomialRobustEstimator(final List<PolynomialEvaluation> evaluations) {
         super(evaluations);
         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 PROMedSPolynomialRobustEstimator(final PolynomialRobustEstimatorListener listener) {
         super(listener);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Constructor.
      *
      * @param degree      degree of polynomial to be estimated.
      * @param evaluations collection of polynomial evaluations.
      * @throws IllegalArgumentException if provided degree is less than 1 or if
      *                                  provided number of evaluations is less than the required minimum for
      *                                  provided degree.
      */
     public PROMedSPolynomialRobustEstimator(final int degree, final List<PolynomialEvaluation> evaluations) {
         super(degree, evaluations);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Constructor.
      *
      * @param degree   degree of polynomial to be estimated.
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      * @throws IllegalArgumentException if provided degree is less than 1.
      */
     public PROMedSPolynomialRobustEstimator(final int degree, final PolynomialRobustEstimatorListener listener) {
         super(degree, listener);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Constructor.
      *
      * @param evaluations collection of polynomial evaluations.
      * @param listener    listener to be notified of events such as when estimation
      *                    starts, ends or its progress significantly changes.
      * @throws IllegalArgumentException if provided number of evaluations is
      *                                  less than the required minimum.
      */
     public PROMedSPolynomialRobustEstimator(
             final List<PolynomialEvaluation> evaluations, final PolynomialRobustEstimatorListener listener) {
         super(evaluations, listener);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Constructor.
      *
      * @param degree      degree of polynomial to be estimated.
      * @param evaluations collection of polynomial evaluations.
      * @param listener    listener to be notified of events such as when estimation
      *                    starts, ends or its progress significantly changes.
      * @throws IllegalArgumentException if provided degree is less than 1 or if
      *                                  provided number of evaluations is less than the required minimum for
      *                                  provided degree.
      */
     public PROMedSPolynomialRobustEstimator(
             final int degree, final List<PolynomialEvaluation> evaluations,
             final PolynomialRobustEstimatorListener listener) {
         super(degree, evaluations, listener);
         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;
     }
 
     /**
      * Returns quality scores corresponding to each provided point.
      * The larger the score value the better the quality of the sampled point
      *
      * @return quality scores corresponding to each point
      */
     @Override
     public double[] getQualityScores() {
         return qualityScores;
     }
 
     /**
      * Sets quality scores corresponding to each provided point.
      * The larger the score value the better the quality of the sampled point.
      *
      * @param qualityScores quality scores corresponding to each point.
      * @throws LockedException          if robust estimator is locked because an
      *                                  estimation is already in progress.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than required minimum size.
      */
     @Override
     public void setQualityScores(final double[] qualityScores) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         internalSetQualityScores(qualityScores);
     }
 
     /**
      * Indicates if estimator is ready to start the polynomial estimation.
      * This is true when input data (i.e. polynomial evaluations and quality
      * scores) are provided and enough data is available.
      *
      * @return true if estimator is ready, false otherwise
      */
     @Override
     public boolean isReady() {
         return super.isReady() && qualityScores != null && qualityScores.length == evaluations.size();
     }
 
     /**
      * Estimates polynomial.
      *
      * @return estimated polynomial.
      * @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 other reason
      *                                  (i.e. numerical instability, no solution available, etc).
      */
     @Override
     public Polynomial estimate() throws LockedException, NotReadyException, RobustEstimatorException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (!isReady()) {
             throw new NotReadyException();
         }
 
         final PROMedSRobustEstimator<Polynomial> innerEstimator = new PROMedSRobustEstimator<>(
                 new PROMedSRobustEstimatorListener<>() {
 
                     // subset of evaluations picked on each iteration
                     private final List<PolynomialEvaluation> subsetEvaluations = new ArrayList<>();
 
                     @Override
                     public double getThreshold() {
                         return stopThreshold;
                     }
 
                     @Override
                     public int getTotalSamples() {
                         return evaluations.size();
                     }
 
                     @Override
                     public int getSubsetSize() {
                         return polynomialEstimator.getMinNumberOfEvaluations();
                     }
 
                     @SuppressWarnings("DuplicatedCode")
                     @Override
                     public void estimatePreliminarSolutions(
                             final int[] samplesIndices, final List<Polynomial> solutions) {
                         subsetEvaluations.clear();
                         for (var samplesIndex : samplesIndices) {
                             subsetEvaluations.add(evaluations.get(samplesIndex));
                         }
 
                         try {
                             polynomialEstimator.setLMSESolutionAllowed(false);
                             polynomialEstimator.setEvaluations(subsetEvaluations);
 
                             final var polynomial = polynomialEstimator.estimate();
                             solutions.add(polynomial);
                         } catch (final Exception e) {
                             // if anything fails, no solution is added
                         }
                     }
 
                     @Override
                     public double computeResidual(final Polynomial currentEstimation, int i) {
                         final var eval = evaluations.get(i);
                         return getDistance(eval, currentEstimation);
                     }
 
                     @Override
                     public boolean isReady() {
                         return PROMedSPolynomialRobustEstimator.this.isReady();
                     }
 
                     @Override
                     public void onEstimateStart(final RobustEstimator<Polynomial> estimator) {
                         if (listener != null) {
                             listener.onEstimateStart(PROMedSPolynomialRobustEstimator.this);
                         }
                     }
 
                     @Override
                     public void onEstimateEnd(final RobustEstimator<Polynomial> estimator) {
                         if (listener != null) {
                             listener.onEstimateEnd(PROMedSPolynomialRobustEstimator.this);
                         }
                     }
 
                     @Override
                     public void onEstimateNextIteration(
                             final RobustEstimator<Polynomial> estimator, final int iteration) {
                         if (listener != null) {
                             listener.onEstimateNextIteration(PROMedSPolynomialRobustEstimator.this, iteration);
                         }
                     }
 
                     @Override
                     public void onEstimateProgressChange(
                             final RobustEstimator<Polynomial> estimator, final float progress) {
                         if (listener != null) {
                             listener.onEstimateProgressChange(PROMedSPolynomialRobustEstimator.this, progress);
                         }
                     }
 
                     @Override
                     public double[] getQualityScores() {
                         return qualityScores;
                     }
                 });
 
         try {
             locked = true;
             innerEstimator.setConfidence(confidence);
             innerEstimator.setMaxIterations(maxIterations);
             innerEstimator.setProgressDelta(progressDelta);
             return innerEstimator.estimate();
         } finally {
             locked = false;
         }
     }
 
     /**
      * Returns method being used for robust estimation.
      *
      * @return method being used for robust estimation.
      */
     @Override
     public RobustEstimatorMethod getMethod() {
         return RobustEstimatorMethod.PROMEDS;
     }
 
     /**
      * Sets quality scores corresponding to each provided polynomial evaluation.
      * This method is used internally and does not check whether instance is
      * locked or not
      *
      * @param qualityScores quality scores to be set
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than required minimum size.
      */
     private void internalSetQualityScores(final double[] qualityScores) {
         if (qualityScores.length < polynomialEstimator.getMinNumberOfEvaluations()) {
             throw new IllegalArgumentException();
         }
 
         this.qualityScores = qualityScores;
     }
 }