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-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/statistics/DistanceStatisticsWithClasses.java302
1 files changed, 171 insertions, 131 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/statistics/DistanceStatisticsWithClasses.java b/src/de/lmu/ifi/dbs/elki/algorithm/statistics/DistanceStatisticsWithClasses.java
index d6ce6a15..ebf588b6 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/statistics/DistanceStatisticsWithClasses.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/statistics/DistanceStatisticsWithClasses.java
@@ -42,6 +42,7 @@ import de.lmu.ifi.dbs.elki.database.ids.ArrayModifiableDBIDs;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
+import de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDPair;
import de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
@@ -52,10 +53,11 @@ import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.logging.progress.StepProgress;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
-import de.lmu.ifi.dbs.elki.math.histograms.AggregatingHistogram;
-import de.lmu.ifi.dbs.elki.math.histograms.FlexiHistogram;
import de.lmu.ifi.dbs.elki.result.CollectionResult;
import de.lmu.ifi.dbs.elki.result.HistogramResult;
+import de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.AbstractObjDynamicHistogram;
+import de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.LongArrayStaticHistogram;
+import de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.ObjHistogram;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.exceptions.ExceptionMessages;
@@ -66,14 +68,13 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameteriz
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Flag;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Parameter;
-import de.lmu.ifi.dbs.elki.utilities.pairs.DoubleObjPair;
-import de.lmu.ifi.dbs.elki.utilities.pairs.Pair;
/**
* Algorithm to gather statistics over the distance distribution in the data
* set.
*
* @author Erich Schubert
+ *
* @param <O> Object type
* @param <D> Distance type
*/
@@ -84,22 +85,22 @@ public class DistanceStatisticsWithClasses<O, D extends NumberDistance<D, ?>> ex
/**
* The logger for this class.
*/
- private static final Logging logger = Logging.getLogger(DistanceStatisticsWithClasses.class);
+ private static final Logging LOG = Logging.getLogger(DistanceStatisticsWithClasses.class);
/**
* Flag to compute exact value range for binning.
*/
- public static final OptionID EXACT_ID = OptionID.getOrCreateOptionID("diststat.exact", "In a first pass, compute the exact minimum and maximum, at the cost of O(2*n*n) instead of O(n*n). The number of resulting bins is guaranteed to be as requested.");
+ public static final OptionID EXACT_ID = new OptionID("diststat.exact", "In a first pass, compute the exact minimum and maximum, at the cost of O(2*n*n) instead of O(n*n). The number of resulting bins is guaranteed to be as requested.");
/**
- * Flag to enable sampling
+ * Flag to enable sampling.
*/
- public static final OptionID SAMPLING_ID = OptionID.getOrCreateOptionID("diststat.sampling", "Enable sampling of O(n) size to determine the minimum and maximum distances approximately. The resulting number of bins can be larger than the given n.");
+ public static final OptionID SAMPLING_ID = new OptionID("diststat.sampling", "Enable sampling of O(n) size to determine the minimum and maximum distances approximately. The resulting number of bins can be larger than the given n.");
/**
* Option to configure the number of bins to use.
*/
- public static final OptionID HISTOGRAM_BINS_ID = OptionID.getOrCreateOptionID("diststat.bins", "Number of bins to use in the histogram. By default, it is only guaranteed to be within 1*n and 2*n of the given number.");
+ public static final OptionID HISTOGRAM_BINS_ID = new OptionID("diststat.bins", "Number of bins to use in the histogram. By default, it is only guaranteed to be within 1*n and 2*n of the given number.");
/**
* Number of bins to use in sampling.
@@ -107,12 +108,12 @@ public class DistanceStatisticsWithClasses<O, D extends NumberDistance<D, ?>> ex
private int numbin;
/**
- * Sampling
+ * Sampling flag.
*/
private boolean sampling = false;
/**
- * Sampling
+ * Compute exactly (slower).
*/
private boolean exact = false;
@@ -136,7 +137,7 @@ public class DistanceStatisticsWithClasses<O, D extends NumberDistance<D, ?>> ex
final Relation<O> relation = database.getRelation(getInputTypeRestriction()[0]);
final DistanceQuery<O, D> distFunc = database.getDistanceQuery(relation, getDistanceFunction());
- final StepProgress stepprog = logger.isVerbose() ? new StepProgress("Distance statistics", 2) : null;
+ final StepProgress stepprog = LOG.isVerbose() ? new StepProgress("Distance statistics", 2) : null;
// determine binning ranges.
DoubleMinMax gminmax = new DoubleMinMax();
@@ -157,43 +158,71 @@ public class DistanceStatisticsWithClasses<O, D extends NumberDistance<D, ?>> ex
MeanVariance momax = new MeanVariance();
MeanVariance modif = new MeanVariance();
// Histogram
- final AggregatingHistogram<Pair<Long, Long>, Pair<Long, Long>> histogram;
- if(stepprog != null) {
- stepprog.beginStep(1, "Prepare histogram.", logger);
+ final ObjHistogram<long[]> histogram;
+ if (stepprog != null) {
+ stepprog.beginStep(1, "Prepare histogram.", LOG);
}
- if(exact) {
+ if (exact) {
gminmax = exactMinMax(relation, distFunc);
- histogram = AggregatingHistogram.LongSumLongSumHistogram(numbin, gminmax.getMin(), gminmax.getMax());
- }
- else if(sampling) {
+ histogram = new LongArrayStaticHistogram(numbin, gminmax.getMin(), gminmax.getMax(), 2);
+ } else if (sampling) {
gminmax = sampleMinMax(relation, distFunc);
- histogram = AggregatingHistogram.LongSumLongSumHistogram(numbin, gminmax.getMin(), gminmax.getMax());
- }
- else {
- histogram = FlexiHistogram.LongSumLongSumHistogram(numbin);
+ histogram = new LongArrayStaticHistogram(numbin, gminmax.getMin(), gminmax.getMax(), 2);
+ } else {
+ histogram = new AbstractObjDynamicHistogram<long[]>(numbin) {
+ @Override
+ protected long[] downsample(Object[] data, int start, int end, int size) {
+ long[] ret = new long[2];
+ for (int i = start; i < end; i++) {
+ long[] existing = (long[]) data[i];
+ if (existing != null) {
+ for (int c = 0; c < 2; c++) {
+ ret[c] += existing[c];
+ }
+ }
+ }
+ return ret;
+ }
+
+ @Override
+ protected long[] aggregate(long[] first, long[] second) {
+ for (int c = 0; c < 2; c++) {
+ first[c] += second[c];
+ }
+ return first;
+ }
+
+ @Override
+ protected long[] cloneForCache(long[] data) {
+ return data.clone();
+ }
+
+ @Override
+ protected long[] makeObject() {
+ return new long[2];
+ }
+ };
}
- if(stepprog != null) {
- stepprog.beginStep(2, "Build histogram.", logger);
+ if (stepprog != null) {
+ stepprog.beginStep(2, "Build histogram.", LOG);
}
- final FiniteProgress progress = logger.isVerbose() ? new FiniteProgress("Distance computations", relation.size(), logger) : null;
+ final FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("Distance computations", relation.size(), LOG) : null;
// iterate per cluster
- final Pair<Long, Long> incFirst = new Pair<Long, Long>(1L, 0L);
- final Pair<Long, Long> incSecond = new Pair<Long, Long>(0L, 1L);
- for(Cluster<?> c1 : split) {
- for(DBIDIter iter = c1.getIDs().iter(); iter.valid(); iter.advance()) {
- DBID id1 = iter.getDBID();
+ final long[] incFirst = new long[] { 1L, 0L };
+ final long[] incSecond = new long[] { 0L, 1L };
+ for (Cluster<?> c1 : split) {
+ for (DBIDIter id1 = c1.getIDs().iter(); id1.valid(); id1.advance()) {
// in-cluster distances
DoubleMinMax iminmax = new DoubleMinMax();
- for(DBIDIter iter2 = c1.getIDs().iter(); iter2.valid(); iter2.advance()) {
- DBID id2 = iter2.getDBID();
+ for (DBIDIter iter2 = c1.getIDs().iter(); iter2.valid(); iter2.advance()) {
// skip the point itself.
- if(id1.sameDBID(id2)) {
+ if (DBIDUtil.equal(id1, iter2)) {
continue;
}
- double d = distFunc.distance(id1, id2).doubleValue();
+ double d = distFunc.distance(id1, iter2).doubleValue();
- histogram.aggregate(d, incFirst);
+ histogram.putData(d, incFirst);
iminmax.put(d);
}
@@ -207,19 +236,18 @@ public class DistanceStatisticsWithClasses<O, D extends NumberDistance<D, ?>> ex
// other-cluster distances
DoubleMinMax ominmax = new DoubleMinMax();
- for(Cluster<?> c2 : split) {
- if(c2 == c1) {
+ for (Cluster<?> c2 : split) {
+ if (c2 == c1) {
continue;
}
- for(DBIDIter iter2 = c2.getIDs().iter(); iter2.valid(); iter2.advance()) {
- DBID id2 = iter2.getDBID();
+ for (DBIDIter iter2 = c2.getIDs().iter(); iter2.valid(); iter2.advance()) {
// skip the point itself (shouldn't happen though)
- if(id1.sameDBID(id2)) {
+ if (DBIDUtil.equal(id1, iter2)) {
continue;
}
- double d = distFunc.distance(id1, id2).doubleValue();
+ double d = distFunc.distance(id1, iter2).doubleValue();
- histogram.aggregate(d, incSecond);
+ histogram.putData(d, incSecond);
ominmax.put(d);
}
@@ -231,38 +259,39 @@ public class DistanceStatisticsWithClasses<O, D extends NumberDistance<D, ?>> ex
// min/max
gominmax.put(ominmax.getMin());
gominmax.put(ominmax.getMax());
- if(progress != null) {
- progress.incrementProcessed(logger);
+ if (progress != null) {
+ progress.incrementProcessed(LOG);
}
}
}
- if(progress != null) {
- progress.ensureCompleted(logger);
+ if (progress != null) {
+ progress.ensureCompleted(LOG);
}
// Update values (only needed for sampling case).
gminmax.setFirst(Math.min(giminmax.getMin(), gominmax.getMin()));
gminmax.setSecond(Math.max(giminmax.getMax(), gominmax.getMax()));
- if(stepprog != null) {
- stepprog.setCompleted(logger);
+ if (stepprog != null) {
+ stepprog.setCompleted(LOG);
}
// count the number of samples we have in the data
long inum = 0;
long onum = 0;
- for(DoubleObjPair<Pair<Long, Long>> ppair : histogram) {
- inum += ppair.getSecond().getFirst();
- onum += ppair.getSecond().getSecond();
+ for (ObjHistogram.Iter<long[]> iter = histogram.iter(); iter.valid(); iter.advance()) {
+ inum += iter.getValue()[0];
+ onum += iter.getValue()[1];
}
long bnum = inum + onum;
Collection<DoubleVector> binstat = new ArrayList<DoubleVector>(numbin);
- for(DoubleObjPair<Pair<Long, Long>> ppair : histogram) {
- final double icof = (inum == 0) ? 0 : ((double) ppair.getSecond().getFirst()) / inum / histogram.getBinsize();
- final double icaf = ((double) ppair.getSecond().getFirst()) / bnum / histogram.getBinsize();
- final double ocof = (onum == 0) ? 0 : ((double) ppair.getSecond().getSecond()) / onum / histogram.getBinsize();
- final double ocaf = ((double) ppair.getSecond().getSecond()) / bnum / histogram.getBinsize();
- DoubleVector row = new DoubleVector(new double[] { ppair.first, icof, icaf, ocof, ocaf });
+ for (ObjHistogram.Iter<long[]> iter = histogram.iter(); iter.valid(); iter.advance()) {
+ final long[] value = iter.getValue();
+ final double icof = (inum == 0) ? 0 : ((double) value[0]) / inum / histogram.getBinsize();
+ final double icaf = ((double) value[0]) / bnum / histogram.getBinsize();
+ final double ocof = (onum == 0) ? 0 : ((double) value[1]) / onum / histogram.getBinsize();
+ final double ocaf = ((double) value[1]) / bnum / histogram.getBinsize();
+ DoubleVector row = new DoubleVector(new double[] { iter.getCenter(), icof, icaf, ocof, ocaf });
binstat.add(row);
}
HistogramResult<DoubleVector> result = new HistogramResult<DoubleVector>("Distance Histogram", "distance-histogram", binstat);
@@ -278,111 +307,121 @@ public class DistanceStatisticsWithClasses<O, D extends NumberDistance<D, ?>> ex
return result;
}
- private DoubleMinMax sampleMinMax(Relation<O> database, DistanceQuery<O, D> distFunc) {
- int size = database.size();
+ /**
+ * Estimate minimum and maximum via sampling.
+ *
+ * @param relation Relation to process
+ * @param distFunc Distance function to use
+ * @return Minimum and maximum
+ */
+ private DoubleMinMax sampleMinMax(Relation<O> relation, DistanceQuery<O, D> distFunc) {
+ int size = relation.size();
Random rnd = new Random();
// estimate minimum and maximum.
- int k = (int) Math.max(25, Math.pow(database.size(), 0.2));
- TreeSet<DoubleObjPair<DBID>> minhotset = new TreeSet<DoubleObjPair<DBID>>();
- TreeSet<DoubleObjPair<DBID>> maxhotset = new TreeSet<DoubleObjPair<DBID>>(Collections.reverseOrder());
+ int k = (int) Math.max(25, Math.pow(relation.size(), 0.2));
+ TreeSet<DoubleDBIDPair> minhotset = new TreeSet<DoubleDBIDPair>();
+ TreeSet<DoubleDBIDPair> maxhotset = new TreeSet<DoubleDBIDPair>(Collections.reverseOrder());
- int randomsize = (int) Math.max(25, Math.pow(database.size(), 0.2));
+ int randomsize = (int) Math.max(25, Math.pow(relation.size(), 0.2));
double rprob = ((double) randomsize) / size;
ArrayModifiableDBIDs randomset = DBIDUtil.newArray(randomsize);
- DBIDIter iter = database.iterDBIDs();
- if(!iter.valid()) {
+ DBIDIter iter = relation.iterDBIDs();
+ if (!iter.valid()) {
throw new IllegalStateException(ExceptionMessages.DATABASE_EMPTY);
}
- DBID firstid = iter.getDBID();
+ DBID firstid = DBIDUtil.deref(iter);
iter.advance();
- minhotset.add(new DoubleObjPair<DBID>(Double.MAX_VALUE, firstid));
- maxhotset.add(new DoubleObjPair<DBID>(Double.MIN_VALUE, firstid));
- while(iter.valid()) {
- DBID id1 = iter.getDBID();
- iter.advance();
+ minhotset.add(DBIDUtil.newPair(Double.MAX_VALUE, firstid));
+ maxhotset.add(DBIDUtil.newPair(Double.MIN_VALUE, firstid));
+ for (; iter.valid(); iter.advance()) {
// generate candidates for min distance.
- ArrayList<DoubleObjPair<DBID>> np = new ArrayList<DoubleObjPair<DBID>>(k * 2 + randomsize * 2);
- for(DoubleObjPair<DBID> pair : minhotset) {
- DBID id2 = pair.getSecond();
+ ArrayList<DoubleDBIDPair> np = new ArrayList<DoubleDBIDPair>(k * 2 + randomsize * 2);
+ for (DoubleDBIDPair pair : minhotset) {
// skip the object itself
- if(id1.compareTo(id2) == 0) {
+ if (DBIDUtil.equal(iter, pair)) {
continue;
}
- double d = distFunc.distance(id1, id2).doubleValue();
- np.add(new DoubleObjPair<DBID>(d, id1));
- np.add(new DoubleObjPair<DBID>(d, id2));
+ double d = distFunc.distance(iter, pair).doubleValue();
+ np.add(DBIDUtil.newPair(d, iter));
+ np.add(DBIDUtil.newPair(d, pair));
}
- for(DBIDIter iter2 = randomset.iter(); iter2.valid(); iter2.advance()) {
- DBID id2 = iter2.getDBID();
- double d = distFunc.distance(id1, id2).doubleValue();
- np.add(new DoubleObjPair<DBID>(d, id1));
- np.add(new DoubleObjPair<DBID>(d, id2));
+ for (DBIDIter iter2 = randomset.iter(); iter2.valid(); iter2.advance()) {
+ double d = distFunc.distance(iter, iter2).doubleValue();
+ np.add(DBIDUtil.newPair(d, iter));
+ np.add(DBIDUtil.newPair(d, iter2));
}
minhotset.addAll(np);
shrinkHeap(minhotset, k);
// generate candidates for max distance.
- ArrayList<DoubleObjPair<DBID>> np2 = new ArrayList<DoubleObjPair<DBID>>(k * 2 + randomsize * 2);
- for(DoubleObjPair<DBID> pair : minhotset) {
- DBID id2 = pair.getSecond();
+ ArrayList<DoubleDBIDPair> np2 = new ArrayList<DoubleDBIDPair>(k * 2 + randomsize * 2);
+ for (DoubleDBIDPair pair : minhotset) {
// skip the object itself
- if(id1.compareTo(id2) == 0) {
+ if (DBIDUtil.equal(iter, pair)) {
continue;
}
- double d = distFunc.distance(id1, id2).doubleValue();
- np2.add(new DoubleObjPair<DBID>(d, id1));
- np2.add(new DoubleObjPair<DBID>(d, id2));
+ double d = distFunc.distance(iter, pair).doubleValue();
+ np2.add(DBIDUtil.newPair(d, iter));
+ np2.add(DBIDUtil.newPair(d, pair));
}
- for(DBIDIter iter2 = randomset.iter(); iter2.valid(); iter2.advance()) {
- DBID id2 = iter2.getDBID();
- double d = distFunc.distance(id1, id2).doubleValue();
- np.add(new DoubleObjPair<DBID>(d, id1));
- np.add(new DoubleObjPair<DBID>(d, id2));
+ for (DBIDIter iter2 = randomset.iter(); iter2.valid(); iter2.advance()) {
+ double d = distFunc.distance(iter, iter2).doubleValue();
+ np.add(DBIDUtil.newPair(d, iter));
+ np.add(DBIDUtil.newPair(d, iter2));
}
maxhotset.addAll(np2);
shrinkHeap(maxhotset, k);
// update random set
- if(randomset.size() < randomsize) {
- randomset.add(id1);
- }
- else if(rnd.nextDouble() < rprob) {
- randomset.set((int) Math.floor(rnd.nextDouble() * randomsize), id1);
+ if (randomset.size() < randomsize) {
+ randomset.add(iter);
+ } else if (rnd.nextDouble() < rprob) {
+ randomset.set((int) Math.floor(rnd.nextDouble() * randomsize), iter);
}
}
- return new DoubleMinMax(minhotset.first().first, maxhotset.first().first);
+ return new DoubleMinMax(minhotset.first().doubleValue(), maxhotset.first().doubleValue());
}
- private DoubleMinMax exactMinMax(Relation<O> database, DistanceQuery<O, D> distFunc) {
+ /**
+ * Compute the exact maximum and minimum.
+ *
+ * @param relation Relation to process
+ * @param distFunc Distance function
+ * @return Exact maximum and minimum
+ */
+ private DoubleMinMax exactMinMax(Relation<O> relation, DistanceQuery<O, D> distFunc) {
DoubleMinMax minmax = new DoubleMinMax();
// find exact minimum and maximum first.
- for(DBIDIter iditer = database.iterDBIDs(); iditer.valid(); iditer.advance()) {
- DBID id1 = iditer.getDBID();
- for(DBIDIter iditer2 = database.iterDBIDs(); iditer2.valid(); iditer2.advance()) {
- DBID id2 = iditer2.getDBID();
+ for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ for (DBIDIter iditer2 = relation.iterDBIDs(); iditer2.valid(); iditer2.advance()) {
// skip the point itself.
- if(id1.compareTo(id2) == 0) {
+ if (DBIDUtil.equal(iditer, iditer2)) {
continue;
}
- double d = distFunc.distance(id1, id2).doubleValue();
+ double d = distFunc.distance(iditer, iditer2).doubleValue();
minmax.put(d);
}
}
return minmax;
}
- private void shrinkHeap(TreeSet<DoubleObjPair<DBID>> hotset, int k) {
+ /**
+ * Shrink the heap of "hot" (extreme) items.
+ *
+ * @param hotset Set of hot items
+ * @param k target size
+ */
+ private static void shrinkHeap(TreeSet<DoubleDBIDPair> hotset, int k) {
// drop duplicates
ModifiableDBIDs seenids = DBIDUtil.newHashSet(2 * k);
int cnt = 0;
- for(Iterator<DoubleObjPair<DBID>> i = hotset.iterator(); i.hasNext();) {
- DoubleObjPair<DBID> p = i.next();
- if(cnt > k || seenids.contains(p.getSecond())) {
+ for (Iterator<DoubleDBIDPair> i = hotset.iterator(); i.hasNext();) {
+ DoubleDBIDPair p = i.next();
+ if (cnt > k || seenids.contains(p)) {
i.remove();
- }
- else {
- seenids.add(p.getSecond());
+ } else {
+ seenids.add(p);
cnt++;
}
}
@@ -395,7 +434,7 @@ public class DistanceStatisticsWithClasses<O, D extends NumberDistance<D, ?>> ex
@Override
protected Logging getLogger() {
- return logger;
+ return LOG;
}
/**
@@ -412,36 +451,37 @@ public class DistanceStatisticsWithClasses<O, D extends NumberDistance<D, ?>> ex
private int numbin = 20;
/**
- * Sampling
+ * Sampling.
*/
private boolean sampling = false;
/**
- * Sampling
+ * Exactness flag.
*/
private boolean exact = false;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
- final IntParameter numbinP = new IntParameter(HISTOGRAM_BINS_ID, new GreaterEqualConstraint(2), 20);
- if(config.grab(numbinP)) {
+ final IntParameter numbinP = new IntParameter(HISTOGRAM_BINS_ID, 20);
+ numbinP.addConstraint(new GreaterEqualConstraint(2));
+ if (config.grab(numbinP)) {
numbin = numbinP.getValue();
}
- final Flag EXACT_FLAG = new Flag(EXACT_ID);
- if(config.grab(EXACT_FLAG)) {
- exact = EXACT_FLAG.getValue();
+ final Flag exactF = new Flag(EXACT_ID);
+ if (config.grab(exactF)) {
+ exact = exactF.getValue();
}
- final Flag SAMPLING_FLAG = new Flag(SAMPLING_ID);
- if(config.grab(SAMPLING_FLAG)) {
- sampling = SAMPLING_FLAG.getValue();
+ final Flag samplingF = new Flag(SAMPLING_ID);
+ if (config.grab(samplingF)) {
+ sampling = samplingF.getValue();
}
- ArrayList<Parameter<?, ?>> exclusive = new ArrayList<Parameter<?, ?>>();
- exclusive.add(EXACT_FLAG);
- exclusive.add(SAMPLING_FLAG);
+ ArrayList<Parameter<?>> exclusive = new ArrayList<Parameter<?>>();
+ exclusive.add(exactF);
+ exclusive.add(samplingF);
config.checkConstraint(new OnlyOneIsAllowedToBeSetGlobalConstraint(exclusive));
}
@@ -450,4 +490,4 @@ public class DistanceStatisticsWithClasses<O, D extends NumberDistance<D, ?>> ex
return new DistanceStatisticsWithClasses<O, D>(distanceFunction, numbin, exact, sampling);
}
}
-} \ No newline at end of file
+}
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-17 20:05:37 +0000