LMedSPolynomialRobustEstimator.java
/*
* 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.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.ArrayList;
import java.util.List;
/**
* Finds the best polynomial using LMedS algorithm.
*/
public class LMedSPolynomialRobustEstimator 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 solutions is found
* that generates a threshold below this value, the algorithm will stop.
* Threshold will be used to compare either algebraic or geometric distance
* of estimated polynomial respect each provided evaluation.
*/
public static final double DEFAULT_STOP_THRESHOLD = 1e-6;
/**
* Minimum value that can be set as stop threshold.
* Threshold must be strictly greater than 0.0.
*/
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 case 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 sopt 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 LMedSPolynomialRobustEstimator() {
super();
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param degree degree of polynomial to be estimated.
* @throws IllegalArgumentException if provided degree is less than 1.
*/
public LMedSPolynomialRobustEstimator(final int degree) {
super(degree);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param evaluations collections of polynomial evaluations.
* @throws IllegalArgumentException if provided number of evaluations is
* less than the required minimum.
*/
public LMedSPolynomialRobustEstimator(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 LMedSPolynomialRobustEstimator(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 LMedSPolynomialRobustEstimator(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 LMedSPolynomialRobustEstimator(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 LMedSPolynomialRobustEstimator(
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 LMedSPolynomialRobustEstimator(
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;
}
/**
* 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 LMedSRobustEstimator<Polynomial> innerEstimator =
new LMedSRobustEstimator<>(new LMedSRobustEstimatorListener<>() {
// subset of evaluations picked on each iteration
private final List<PolynomialEvaluation> subsetEvaluations = new ArrayList<>();
@Override
public int getTotalSamples() {
return evaluations.size();
}
@Override
public int getSubsetSize() {
return polynomialEstimator.getMinNumberOfEvaluations();
}
@Override
public void estimatePreliminarSolutions(
final int[] samplesIndices, final List<Polynomial> solutions) {
subsetEvaluations.clear();
for (final 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, final int i) {
final var eval = evaluations.get(i);
return getDistance(eval, currentEstimation);
}
@Override
public boolean isReady() {
return LMedSPolynomialRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator<Polynomial> estimator) {
if (listener != null) {
listener.onEstimateStart(LMedSPolynomialRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(final RobustEstimator<Polynomial> estimator) {
if (listener != null) {
listener.onEstimateEnd(LMedSPolynomialRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator<Polynomial> estimator, final int iteration) {
if (listener != null) {
listener.onEstimateNextIteration(LMedSPolynomialRobustEstimator.this, iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator<Polynomial> estimator, final float progress) {
if (listener != null) {
listener.onEstimateProgressChange(LMedSPolynomialRobustEstimator.this, progress);
}
}
});
try {
locked = true;
innerEstimator.setConfidence(confidence);
innerEstimator.setMaxIterations(maxIterations);
innerEstimator.setProgressDelta(progressDelta);
innerEstimator.setStopThreshold(stopThreshold);
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.LMEDS;
}
}