PROSACPolynomialRobustEstimator.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.PROSACRobustEstimator;
import com.irurueta.numerical.robust.PROSACRobustEstimatorListener;
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 PROSAC algorithm.
*/
public class PROSACPolynomialRobustEstimator extends PolynomialRobustEstimator {
/**
* Constant defining default threshold to determine whether polynomials are
* inliers or not.
* Threshold will be used to compare either algebraic or geometric distance
* of estimated polynomial respect each provided evaluation.
*/
public static final double DEFAULT_THRESHOLD = 1e-6;
/**
* Minimum value that can be set as threshold.
* Threshold must be strictly greater than 0.0.
*/
public static final double MIN_THRESHOLD = 0.0;
/**
* Threshold to determine whether polynomial evaluations are inlers or not
* when testing possible estimation solutions
*/
private double threshold;
/**
* 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 PROSACPolynomialRobustEstimator() {
super();
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param degree degree of polynomial to be estimated.
* @throws IllegalArgumentException if provided degree is less than 1.
*/
public PROSACPolynomialRobustEstimator(final int degree) {
super(degree);
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param evaluations collection of polynomial evaluations.
* @throws IllegalArgumentException if provided number of evaluations is
* less than the required minimum.
*/
public PROSACPolynomialRobustEstimator(final List<PolynomialEvaluation> evaluations) {
super(evaluations);
threshold = DEFAULT_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
public PROSACPolynomialRobustEstimator(final PolynomialRobustEstimatorListener listener) {
super(listener);
threshold = DEFAULT_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 PROSACPolynomialRobustEstimator(final int degree, final List<PolynomialEvaluation> evaluations) {
super(degree, evaluations);
threshold = DEFAULT_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 PROSACPolynomialRobustEstimator(final int degree, final PolynomialRobustEstimatorListener listener) {
super(degree, listener);
threshold = DEFAULT_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 PROSACPolynomialRobustEstimator(
final List<PolynomialEvaluation> evaluations, final PolynomialRobustEstimatorListener listener) {
super(evaluations, listener);
threshold = DEFAULT_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 PROSACPolynomialRobustEstimator(
final int degree, final List<PolynomialEvaluation> evaluations,
final PolynomialRobustEstimatorListener listener) {
super(degree, evaluations, listener);
threshold = DEFAULT_THRESHOLD;
}
/**
* Returns threshold to determine whether polynomials are inliers or not
* when testing possible estimation solutions.
*
* @return threshold to determine whether polynomials are inliers or not
* when testing possible estimation solutions.
*/
public double getThreshold() {
return threshold;
}
/**
* Sets threshold to determine whether polynomials are inliers or not when
* testing possible estimation solutions.
*
* @param threshold threshold to determine whether polynomials are inliers
* or not when testing possible estimation solutions.
* @throws IllegalArgumentException if provided value is equal or less than
* zero.
* @throws LockedException if robust estimator is locked.
*/
public void setThreshold(final double threshold) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (threshold <= MIN_THRESHOLD) {
throw new IllegalArgumentException();
}
this.threshold = threshold;
}
/**
* Returns quality scores corresponding to each provided point.
* The larger the score value the betther 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 PROSACRobustEstimator<Polynomial> innerEstimator = new PROSACRobustEstimator<>(
new PROSACRobustEstimatorListener<>() {
// subset of evaluations picked on each iteration
private final List<PolynomialEvaluation> subsetEvaluations = new ArrayList<>();
@Override
public double getThreshold() {
return threshold;
}
@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, final int i) {
final var eval = evaluations.get(i);
return getDistance(eval, currentEstimation);
}
@Override
public boolean isReady() {
return PROSACPolynomialRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator<Polynomial> estimator) {
if (listener != null) {
listener.onEstimateStart(PROSACPolynomialRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(final RobustEstimator<Polynomial> estimator) {
if (listener != null) {
listener.onEstimateEnd(PROSACPolynomialRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator<Polynomial> estimator, final int iteration) {
if (listener != null) {
listener.onEstimateNextIteration(PROSACPolynomialRobustEstimator.this, iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator<Polynomial> estimator, final float progress) {
if (listener != null) {
listener.onEstimateProgressChange(PROSACPolynomialRobustEstimator.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.PROSAC;
}
/**
* 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;
}
}