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* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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 org.apache.commons.math3.geometry.enclosing;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math3.exception.MathInternalError;
import org.apache.commons.math3.geometry.Point;
import org.apache.commons.math3.geometry.Space;
/** Class implementing Emo Welzl algorithm to find the smallest enclosing ball in linear time.
*
* The class implements the algorithm described in paper Smallest
* Enclosing Disks (Balls and Ellipsoids) by Emo Welzl, Lecture Notes in Computer Science
* 555 (1991) 359-370. The pivoting improvement published in the paper Fast and
* Robust Smallest Enclosing Balls, by Bernd Gärtner and further modified in
* paper
* Efficient Computation of Smallest Enclosing Balls in Three Dimensions by Linus Källberg
* to avoid performing local copies of data have been included.
*
* @param Space type.
* @param Point type.
* @since 3.3
*/
public class WelzlEncloser> implements Encloser {
/** Tolerance below which points are consider to be identical. */
private final double tolerance;
/** Generator for balls on support. */
private final SupportBallGenerator generator;
/** Simple constructor.
* @param tolerance below which points are consider to be identical
* @param generator generator for balls on support
*/
public WelzlEncloser(final double tolerance, final SupportBallGenerator generator) {
this.tolerance = tolerance;
this.generator = generator;
}
/** {@inheritDoc} */
public EnclosingBall enclose(final Iterable points) {
if (points == null || !points.iterator().hasNext()) {
// return an empty ball
return generator.ballOnSupport(new ArrayList
());
}
// Emo Welzl algorithm with Bernd Gärtner and Linus Källberg improvements
return pivotingBall(points);
}
/** Compute enclosing ball using Gärtner's pivoting heuristic.
* @param points points to be enclosed
* @return enclosing ball
*/
private EnclosingBall pivotingBall(final Iterable points) {
final P first = points.iterator().next();
final List
extreme = new ArrayList
(first.getSpace().getDimension() + 1);
final List
support = new ArrayList
(first.getSpace().getDimension() + 1);
// start with only first point selected as a candidate support
extreme.add(first);
EnclosingBall ball = moveToFrontBall(extreme, extreme.size(), support);
while (true) {
// select the point farthest to current ball
final P farthest = selectFarthest(points, ball);
if (ball.contains(farthest, tolerance)) {
// we have found a ball containing all points
return ball;
}
// recurse search, restricted to the small subset containing support and farthest point
support.clear();
support.add(farthest);
EnclosingBall savedBall = ball;
ball = moveToFrontBall(extreme, extreme.size(), support);
if (ball.getRadius() < savedBall.getRadius()) {
// this should never happen
throw new MathInternalError();
}
// it was an interesting point, move it to the front
// according to Gärtner's heuristic
extreme.add(0, farthest);
// prune the least interesting points
extreme.subList(ball.getSupportSize(), extreme.size()).clear();
}
}
/** Compute enclosing ball using Welzl's move to front heuristic.
* @param extreme subset of extreme points
* @param nbExtreme number of extreme points to consider
* @param support points that must belong to the ball support
* @return enclosing ball, for the extreme subset only
*/
private EnclosingBall moveToFrontBall(final List extreme, final int nbExtreme,
final List
support) {
// create a new ball on the prescribed support
EnclosingBall ball = generator.ballOnSupport(support);
if (ball.getSupportSize() <= ball.getCenter().getSpace().getDimension()) {
for (int i = 0; i < nbExtreme; ++i) {
final P pi = extreme.get(i);
if (!ball.contains(pi, tolerance)) {
// we have found an outside point,
// enlarge the ball by adding it to the support
support.add(pi);
ball = moveToFrontBall(extreme, i, support);
support.remove(support.size() - 1);
// it was an interesting point, move it to the front
// according to Welzl's heuristic
for (int j = i; j > 0; --j) {
extreme.set(j, extreme.get(j - 1));
}
extreme.set(0, pi);
}
}
}
return ball;
}
/** Select the point farthest to the current ball.
* @param points points to be enclosed
* @param ball current ball
* @return farthest point
*/
public P selectFarthest(final Iterable points, final EnclosingBall ball) {
final P center = ball.getCenter();
P farthest = null;
double dMax = -1.0;
for (final P point : points) {
final double d = point.distance(center);
if (d > dMax) {
farthest = point;
dMax = d;
}
}
return farthest;
}
}