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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java | GeneralizedExtremeValueDistribution.pdf | public static double pdf(double x, double mu, double sigma, double k) {
if(x == Double.POSITIVE_INFINITY || x == Double.NEGATIVE_INFINITY) {
return 0.;
}
x = (x - mu) / sigma;
if(k > 0 || k < 0) {
if(k * x > 1) {
return 0.;
}
double t = FastMath.log(1 - k * x);
retu... | java | public static double pdf(double x, double mu, double sigma, double k) {
if(x == Double.POSITIVE_INFINITY || x == Double.NEGATIVE_INFINITY) {
return 0.;
}
x = (x - mu) / sigma;
if(k > 0 || k < 0) {
if(k * x > 1) {
return 0.;
}
double t = FastMath.log(1 - k * x);
retu... | [
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java | GeneralizedExtremeValueDistribution.cdf | public static double cdf(double val, double mu, double sigma, double k) {
final double x = (val - mu) / sigma;
if(k > 0 || k < 0) {
if(k * x > 1) {
return k > 0 ? 1 : 0;
}
return FastMath.exp(-FastMath.exp(FastMath.log(1 - k * x) / k));
}
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return Fas... | java | public static double cdf(double val, double mu, double sigma, double k) {
final double x = (val - mu) / sigma;
if(k > 0 || k < 0) {
if(k * x > 1) {
return k > 0 ? 1 : 0;
}
return FastMath.exp(-FastMath.exp(FastMath.log(1 - k * x) / k));
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return Fas... | [
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/GeneralizedExtremeValueDistribution.java | GeneralizedExtremeValueDistribution.quantile | public static double quantile(double val, double mu, double sigma, double k) {
if(val < 0.0 || val > 1.0) {
return Double.NaN;
}
if(k < 0) {
return mu + sigma * Math.max((1. - FastMath.pow(-FastMath.log(val), k)) / k, 1. / k);
}
else if(k > 0) {
return mu + sigma * Math.min((1. - F... | java | public static double quantile(double val, double mu, double sigma, double k) {
if(val < 0.0 || val > 1.0) {
return Double.NaN;
}
if(k < 0) {
return mu + sigma * Math.max((1. - FastMath.pow(-FastMath.log(val), k)) / k, 1. / k);
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/RayleighDistribution.java | RayleighDistribution.cdf | public static double cdf(double x, double sigma) {
if(x <= 0.) {
return 0.;
}
final double xs = x / sigma;
return 1. - FastMath.exp(-.5 * xs * xs);
} | java | public static double cdf(double x, double sigma) {
if(x <= 0.) {
return 0.;
}
final double xs = x / sigma;
return 1. - FastMath.exp(-.5 * xs * xs);
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/statistics/distribution/RayleighDistribution.java | RayleighDistribution.quantile | public static double quantile(double val, double sigma) {
if(!(val >= 0.) || !(val <= 1.)) {
return Double.NaN;
}
if(val == 0.) {
return 0.;
}
if(val == 1.) {
return Double.POSITIVE_INFINITY;
}
return sigma * FastMath.sqrt(-2. * FastMath.log(1. - val));
} | java | public static double quantile(double val, double sigma) {
if(!(val >= 0.) || !(val <= 1.)) {
return Double.NaN;
}
if(val == 0.) {
return 0.;
}
if(val == 1.) {
return Double.POSITIVE_INFINITY;
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elki-project/elki | elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/anglebased/ABOD.java | ABOD.run | public OutlierResult run(Database db, Relation<V> relation) {
ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
// Build a kernel matrix, to make O(n^3) slightly less bad.
SimilarityQuery<V> sq = db.getSimilarityQuery(relation, kernelFunction);
KernelMatrix kernelMatrix = new KernelMatrix(sq, ... | java | public OutlierResult run(Database db, Relation<V> relation) {
ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
// Build a kernel matrix, to make O(n^3) slightly less bad.
SimilarityQuery<V> sq = db.getSimilarityQuery(relation, kernelFunction);
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elki-project/elki | elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/anglebased/ABOD.java | ABOD.computeABOF | protected double computeABOF(KernelMatrix kernelMatrix, DBIDRef pA, DBIDArrayIter pB, DBIDArrayIter pC, MeanVariance s) {
s.reset(); // Reused
double simAA = kernelMatrix.getSimilarity(pA, pA);
for(pB.seek(0); pB.valid(); pB.advance()) {
if(DBIDUtil.equal(pB, pA)) {
continue;
}
do... | java | protected double computeABOF(KernelMatrix kernelMatrix, DBIDRef pA, DBIDArrayIter pB, DBIDArrayIter pC, MeanVariance s) {
s.reset(); // Reused
double simAA = kernelMatrix.getSimilarity(pA, pA);
for(pB.seek(0); pB.valid(); pB.advance()) {
if(DBIDUtil.equal(pB, pA)) {
continue;
}
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elki-project/elki | elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/distance/parallel/ParallelKNNWeightOutlier.java | ParallelKNNWeightOutlier.run | public OutlierResult run(Database database, Relation<O> relation) {
DBIDs ids = relation.getDBIDs();
WritableDoubleDataStore store = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_DB);
DistanceQuery<O> distq = database.getDistanceQuery(relation, getDistanceFunction());
KNNQuery<O> knnq = dat... | java | public OutlierResult run(Database database, Relation<O> relation) {
DBIDs ids = relation.getDBIDs();
WritableDoubleDataStore store = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_DB);
DistanceQuery<O> distq = database.getDistanceQuery(relation, getDistanceFunction());
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elki-project/elki | addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java | UKMeans.run | public Clustering<?> run(final Database database, final Relation<DiscreteUncertainObject> relation) {
if(relation.size() <= 0) {
return new Clustering<>("Uk-Means Clustering", "ukmeans-clustering");
}
// Choose initial means randomly
DBIDs sampleids = DBIDUtil.randomSample(relation.getDBIDs(), k, ... | java | public Clustering<?> run(final Database database, final Relation<DiscreteUncertainObject> relation) {
if(relation.size() <= 0) {
return new Clustering<>("Uk-Means Clustering", "ukmeans-clustering");
}
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elki-project/elki | addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java | UKMeans.updateAssignment | protected boolean updateAssignment(DBIDIter iditer, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, int newA) {
final int oldA = assignment.intValue(iditer);
if(oldA == newA) {
return false;
}
clusters.get(newA).add(iditer);
assignment.putInt(iditer, newA);
i... | java | protected boolean updateAssignment(DBIDIter iditer, List<? extends ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, int newA) {
final int oldA = assignment.intValue(iditer);
if(oldA == newA) {
return false;
}
clusters.get(newA).add(iditer);
assignment.putInt(iditer, newA);
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elki-project/elki | addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java | UKMeans.getExpectedRepDistance | protected double getExpectedRepDistance(NumberVector rep, DiscreteUncertainObject uo) {
SquaredEuclideanDistanceFunction euclidean = SquaredEuclideanDistanceFunction.STATIC;
int counter = 0;
double sum = 0.0;
for(int i = 0; i < uo.getNumberSamples(); i++) {
sum += euclidean.distance(rep, uo.getSam... | java | protected double getExpectedRepDistance(NumberVector rep, DiscreteUncertainObject uo) {
SquaredEuclideanDistanceFunction euclidean = SquaredEuclideanDistanceFunction.STATIC;
int counter = 0;
double sum = 0.0;
for(int i = 0; i < uo.getNumberSamples(); i++) {
sum += euclidean.distance(rep, uo.getSam... | [
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elki-project/elki | addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/UKMeans.java | UKMeans.logVarstat | protected void logVarstat(DoubleStatistic varstat, double[] varsum) {
if(varstat != null) {
double s = sum(varsum);
getLogger().statistics(varstat.setDouble(s));
}
} | java | protected void logVarstat(DoubleStatistic varstat, double[] varsum) {
if(varstat != null) {
double s = sum(varsum);
getLogger().statistics(varstat.setDouble(s));
}
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elki-project/elki | elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java | SavedSettingsFile.save | public void save() throws FileNotFoundException {
PrintStream p = new PrintStream(file);
p.println(COMMENT_PREFIX + "Saved ELKI settings. First line is title, remaining lines are parameters.");
for (Pair<String, ArrayList<String>> settings : store) {
p.println(settings.first);
for (String str : ... | java | public void save() throws FileNotFoundException {
PrintStream p = new PrintStream(file);
p.println(COMMENT_PREFIX + "Saved ELKI settings. First line is title, remaining lines are parameters.");
for (Pair<String, ArrayList<String>> settings : store) {
p.println(settings.first);
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elki-project/elki | elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java | SavedSettingsFile.load | public void load() throws FileNotFoundException, IOException {
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ArrayList<String> buf = new ArrayList<>();
while (is.ready()) {
String line = is.readLine();
// skip comments
if (line.startsWith(COMMENT... | java | public void load() throws FileNotFoundException, IOException {
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elki-project/elki | elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java | SavedSettingsFile.remove | public void remove(String key) {
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elki-project/elki | elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/SavedSettingsFile.java | SavedSettingsFile.get | public ArrayList<String> get(String key) {
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java | ORCLUS.run | public Clustering<Model> run(Database database, Relation<V> relation) {
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java | ORCLUS.initialSeeds | private List<ORCLUSCluster> initialSeeds(Relation<V> database, int k) {
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DBIDs randomSample = DBIDUtil.randomSample(database.getDBIDs(), k, rnd);
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java | ORCLUS.assign | private void assign(Relation<V> database, List<ORCLUSCluster> clusters) {
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java | ORCLUS.merge | private void merge(Relation<V> relation, List<ORCLUSCluster> clusters, int k_new, int d_new, IndefiniteProgress cprogress) {
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java | ORCLUS.projectedEnergy | private ProjectedEnergy projectedEnergy(Relation<V> relation, ORCLUSCluster c_i, ORCLUSCluster c_j, int i, int j, int dim) {
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ORCLUSCluster c_ij = union(relation, c_i, c_j, dim);
d... | java | private ProjectedEnergy projectedEnergy(Relation<V> relation, ORCLUSCluster c_i, ORCLUSCluster c_j, int i, int j, int dim) {
NumberVectorDistanceFunction<? super V> distFunc = SquaredEuclideanDistanceFunction.STATIC;
// union of cluster c_i and c_j
ORCLUSCluster c_ij = union(relation, c_i, c_j, dim);
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ORCLUS.java | ORCLUS.union | private ORCLUSCluster union(Relation<V> relation, ORCLUSCluster c1, ORCLUSCluster c2, int dim) {
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java | AnderbergHierarchicalClustering.initializeNNCache | private static void initializeNNCache(double[] scratch, double[] bestd, int[] besti) {
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java | AnderbergHierarchicalClustering.findMerge | protected int findMerge(int size, MatrixParadigm mat, double[] bestd, int[] besti, PointerHierarchyRepresentationBuilder builder) {
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java | AnderbergHierarchicalClustering.merge | protected void merge(int size, MatrixParadigm mat, double[] bestd, int[] besti, PointerHierarchyRepresentationBuilder builder, double mindist, int x, int y) {
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/AnderbergHierarchicalClustering.java | AnderbergHierarchicalClustering.updateCache | private void updateCache(int size, double[] scratch, double[] bestd, int[] besti, int x, int y, int j, double d) {
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// New best
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bestd[j] = d;
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elki-project/elki | addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/VisualizerParameterizer.java | VisualizerParameterizer.newContext | public VisualizerContext newContext(ResultHierarchy hier, Result start) {
Collection<Relation<?>> rels = ResultUtil.filterResults(hier, Relation.class);
for(Relation<?> rel : rels) {
if(samplesize == 0) {
continue;
}
if(!ResultUtil.filterResults(hier, rel, SamplingResult.class).isEmpty... | java | public VisualizerContext newContext(ResultHierarchy hier, Result start) {
Collection<Relation<?>> rels = ResultUtil.filterResults(hier, Relation.class);
for(Relation<?> rel : rels) {
if(samplesize == 0) {
continue;
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elki-project/elki | addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/VisualizerParameterizer.java | VisualizerParameterizer.getTitle | public static String getTitle(Database db, Result result) {
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elki-project/elki | addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/VisualizerParameterizer.java | VisualizerParameterizer.shortenClassname | protected static String shortenClassname(String nam, char c) {
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final int lastdot = nam.lastIndexOf(c);
if(lastdot >= 0) {
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elki-project/elki | elki-docutil/src/main/java/de/lmu/ifi/dbs/elki/application/internal/DocumentParameters.java | DocumentParameters.getRestrictionClass | private static Class<?> getRestrictionClass(OptionID oid, final Parameter<?> firstopt, Map<OptionID, List<Pair<Parameter<?>, Class<?>>>> byopt) {
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Class<?> superclass = getRestrictionClass(firstopt);
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elki-project/elki | elki-docutil/src/main/java/de/lmu/ifi/dbs/elki/application/internal/DocumentParameters.java | DocumentParameters.sorted | private static <T> ArrayList<T> sorted(Collection<T> cls, Comparator<? super T> c) {
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elki-project/elki | addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/AbstractTooltipVisualization.java | AbstractTooltipVisualization.handleHoverEvent | protected void handleHoverEvent(Event evt) {
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Element e = (Element) evt.getTarget();
Node next = e.getNextSibling();
if(next instanceof Element) {
toggleTooltip((Element) next, evt.getType());
}
else {
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if(evt.getTarget() instanceof Element) {
Element e = (Element) evt.getTarget();
Node next = e.getNextSibling();
if(next instanceof Element) {
toggleTooltip((Element) next, evt.getType());
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LoggingUtil.warning("Tooltip... | [
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elki-project/elki | addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/AbstractTooltipVisualization.java | AbstractTooltipVisualization.toggleTooltip | protected void toggleTooltip(Element elem, String type) {
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if(TOOLTIP_HIDDEN.equals(csscls)) {
SVGUtil.setAtt(elem, SVGConstants.SVG_CLASS_ATTRIBUTE, TOOLTIP_VISIBLE);
... | java | protected void toggleTooltip(Element elem, String type) {
String csscls = elem.getAttribute(SVGConstants.SVG_CLASS_ATTRIBUTE);
if(SVGConstants.SVG_MOUSEOVER_EVENT_TYPE.equals(type)) {
if(TOOLTIP_HIDDEN.equals(csscls)) {
SVGUtil.setAtt(elem, SVGConstants.SVG_CLASS_ATTRIBUTE, TOOLTIP_VISIBLE);
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elki-project/elki | elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mkapp/MkAppTree.java | MkAppTree.reverseKNNQuery | @Override
public DoubleDBIDList reverseKNNQuery(DBIDRef id, int k) {
ModifiableDoubleDBIDList result = DBIDUtil.newDistanceDBIDList();
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// push root
pq.add(new MTreeSearchCandidate(0., getRootID(), null, Double.NaN));
// search in tree
... | java | @Override
public DoubleDBIDList reverseKNNQuery(DBIDRef id, int k) {
ModifiableDoubleDBIDList result = DBIDUtil.newDistanceDBIDList();
final Heap<MTreeSearchCandidate> pq = new UpdatableHeap<>();
// push root
pq.add(new MTreeSearchCandidate(0., getRootID(), null, Double.NaN));
// search in tree
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elki-project/elki | elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mkapp/MkAppTree.java | MkAppTree.leafEntryIDs | private void leafEntryIDs(MkAppTreeNode<O> node, ModifiableDBIDs result) {
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MkAppEntry entry = node.getEntry(i);
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}
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for(int i = 0; i < node.getNumEntries();... | java | private void leafEntryIDs(MkAppTreeNode<O> node, ModifiableDBIDs result) {
if(node.isLeaf()) {
for(int i = 0; i < node.getNumEntries(); i++) {
MkAppEntry entry = node.getEntry(i);
result.add(((LeafEntry) entry).getDBID());
}
}
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elki-project/elki | elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mkapp/MkAppTree.java | MkAppTree.approximateKnnDistances | private PolynomialApproximation approximateKnnDistances(double[] knnDistances) {
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int k_0 = 0;
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StringBuilder msg = new StringBuilder();
// count the zero distances (necessary of log-log space is used)
int k_0 = 0;
if(settings.log) {
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java | GrahamScanConvexHull2D.isLeft | protected final int isLeft(double[] a, double[] b, double[] o) {
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if(cross == 0) {
// Compare manhattan distances - same angle!
final double dista = Math.abs(getRX(a, o)) + Math.abs(getRY(a, o));
final double distb = Ma... | java | protected final int isLeft(double[] a, double[] b, double[] o) {
final double cross = getRX(a, o) * getRY(b, o) - getRY(a, o) * getRX(b, o);
if(cross == 0) {
// Compare manhattan distances - same angle!
final double dista = Math.abs(getRX(a, o)) + Math.abs(getRY(a, o));
final double distb = Ma... | [
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java | GrahamScanConvexHull2D.mdist | private double mdist(double[] a, double[] b) {
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java | GrahamScanConvexHull2D.isConvex | private boolean isConvex(double[] a, double[] b, double[] c) {
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double area = (b[0] - a[0]) * factor * (c[1] - a[1]) - (c[0] - a[0]) * factor * (b[1] - a[1]);
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// We're using factor to improve numerical contrast for small polygons.
double area = (b[0] - a[0]) * factor * (c[1] - a[1]) - (c[0] - a[0]) * factor * (b[1] - a[1]);
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java | GrahamScanConvexHull2D.grahamScan | private void grahamScan() {
if(points.size() < 3) {
return;
}
Iterator<double[]> iter = points.iterator();
Stack<double[]> stack = new Stack<>();
// Start with the first two points on the stack
final double[] first = iter.next();
stack.add(first);
while(iter.hasNext()) {
doub... | java | private void grahamScan() {
if(points.size() < 3) {
return;
}
Iterator<double[]> iter = points.iterator();
Stack<double[]> stack = new Stack<>();
// Start with the first two points on the stack
final double[] first = iter.next();
stack.add(first);
while(iter.hasNext()) {
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/GrahamScanConvexHull2D.java | GrahamScanConvexHull2D.getHull | public Polygon getHull() {
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elki-project/elki | elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java | MSTSplit.coverRadius | private static double coverRadius(double[][] matrix, int[] idx, int i) {
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final int idx_i = idx[i];
final double[] row_i = matrix[i];
double m = 0;
for(int j = 0; j < row_i.length; j++) {
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elki-project/elki | elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java | MSTSplit.mstPartition | private static int[] mstPartition(double[][] matrix) {
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final int n = matrix.length;
int[] edges = PrimsMinimumSpanningTree.processDense(matrix);
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elki-project/elki | elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java | MSTSplit.thresholdLength | private static double thresholdLength(double[][] matrix, int[] edges) {
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elki-project/elki | elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java | MSTSplit.edgelength | private static double edgelength(double[][] matrix, int[] edges, int i) {
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elki-project/elki | elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java | MSTSplit.omitEdge | private static void omitEdge(int[] edges, int[] idx, int[] sizes, int omit) {
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elki-project/elki | elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/strategies/split/MSTSplit.java | MSTSplit.follow | private static int follow(int i, int[] partitions) {
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next = partitions[i] = partitions[next];
i = tmp;
}
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int next = partitions[i], tmp;
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elki-project/elki | elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/COP.java | COP.computeCentroid | private static void computeCentroid(double[] centroid, Relation<? extends NumberVector> relation, DBIDs ids) {
Arrays.fill(centroid, 0);
int dim = centroid.length;
for(DBIDIter it = ids.iter(); it.valid(); it.advance()) {
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for(int i = 0; i < dim; i++) {
... | java | private static void computeCentroid(double[] centroid, Relation<? extends NumberVector> relation, DBIDs ids) {
Arrays.fill(centroid, 0);
int dim = centroid.length;
for(DBIDIter it = ids.iter(); it.valid(); it.advance()) {
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elki-project/elki | elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java | QueryUtil.getDistanceQuery | public static <O> DistanceQuery<O> getDistanceQuery(Database database, DistanceFunction<? super O> distanceFunction, Object... hints) {
final Relation<O> objectQuery = database.getRelation(distanceFunction.getInputTypeRestriction(), hints);
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final Relation<O> objectQuery = database.getRelation(distanceFunction.getInputTypeRestriction(), hints);
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elki-project/elki | elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java | QueryUtil.getSimilarityQuery | public static <O> SimilarityQuery<O> getSimilarityQuery(Database database, SimilarityFunction<? super O> similarityFunction, Object... hints) {
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elki-project/elki | elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java | QueryUtil.getRKNNQuery | public static <O> RKNNQuery<O> getRKNNQuery(Relation<O> relation, DistanceFunction<? super O> distanceFunction, Object... hints) {
final DistanceQuery<O> distanceQuery = relation.getDistanceQuery(distanceFunction, hints);
return relation.getRKNNQuery(distanceQuery, hints);
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final DistanceQuery<O> distanceQuery = relation.getDistanceQuery(distanceFunction, hints);
return relation.getRKNNQuery(distanceQuery, hints);
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elki-project/elki | elki-database/src/main/java/de/lmu/ifi/dbs/elki/database/QueryUtil.java | QueryUtil.getLinearScanSimilarityRangeQuery | public static <O> RangeQuery<O> getLinearScanSimilarityRangeQuery(SimilarityQuery<O> simQuery) {
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final PrimitiveSimilarityQuery<O> pdq = (PrimitiveSimilarityQuery<O>) simQuery;
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// Slight optimizations of linear scans
if(simQuery instanceof PrimitiveSimilarityQuery) {
final PrimitiveSimilarityQuery<O> pdq = (PrimitiveSimilarityQuery<O>) simQuery;
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elki-project/elki | elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java | ELKIServiceRegistry.register | protected static void register(Class<?> parent, String cname) {
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if(e == null) {
data.put(parent, e = new Entry());
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elki-project/elki | elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java | ELKIServiceRegistry.register | protected static void register(Class<?> parent, Class<?> clazz) {
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elki-project/elki | elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java | ELKIServiceRegistry.registerAlias | protected static void registerAlias(Class<?> parent, String alias, String cname) {
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elki-project/elki | elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java | ELKIServiceRegistry.tryLoadClass | private static Class<?> tryLoadClass(String value) {
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elki-project/elki | elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java | ELKIServiceRegistry.findAllImplementations | public static List<Class<?>> findAllImplementations(Class<?> restrictionClass) {
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elki-project/elki | elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java | ELKIServiceRegistry.findAllImplementations | public static List<Class<?>> findAllImplementations(Class<?> c, boolean everything, boolean parameterizable) {
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elki-project/elki | elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/ELKIServiceRegistry.java | ELKIServiceRegistry.tryAlternateNames | private static <C> Class<?> tryAlternateNames(Class<? super C> restrictionClass, String value, Entry e) {
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Class<?> clazz = tryLoadClass(buf.append(value).append(FACTORY_POSTFIX).toString());
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Class<?> clazz = tryLoadClass(buf.append(value).append(FACTORY_POSTFIX).toString());
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elki-project/elki | addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/AbstractScatterplotVisualization.java | AbstractScatterplotVisualization.setupCanvas | protected Element setupCanvas() {
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final double margin = context.getStyleLibrary().getSize(StyleLibrary.MARGIN);
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elki-project/elki | elki-input/src/main/java/de/lmu/ifi/dbs/elki/datasource/filter/transform/AbstractSupervisedProjectionVectorFilter.java | AbstractSupervisedProjectionVectorFilter.convertedType | protected SimpleTypeInformation<?> convertedType(SimpleTypeInformation<?> in, NumberVector.Factory<V> factory) {
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elki-project/elki | elki-input/src/main/java/de/lmu/ifi/dbs/elki/datasource/filter/transform/AbstractSupervisedProjectionVectorFilter.java | AbstractSupervisedProjectionVectorFilter.partition | protected <O> Map<O, IntList> partition(List<? extends O> classcolumn) {
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/geometry/XYPlot.java | XYPlot.makeCurve | public Curve makeCurve() {
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elki-project/elki | elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/LogPane.java | LogPane.publish | public void publish(String message, Level level) {
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elki-project/elki | elki-gui-minigui/src/main/java/de/lmu/ifi/dbs/elki/gui/util/LogPane.java | LogPane.publish | protected synchronized void publish(LogRecord record) throws BadLocationException {
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/projection/SNE.java | SNE.optimizeSNE | protected void optimizeSNE(AffinityMatrix pij, double[][] sol) {
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/projection/SNE.java | SNE.computeQij | protected double computeQij(double[][] qij, double[][] solution) {
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}
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double qij_sum = 0;
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/projection/SNE.java | SNE.computeGradient | protected void computeGradient(AffinityMatrix pij, double[][] qij, double qij_isum, double[][] sol, double[] meta) {
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/clustering/KMeansOutlierDetection.java | KMeansOutlierDetection.run | public OutlierResult run(Database database, Relation<O> relation) {
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/linearalgebra/fitting/GaussianFittingFunction.java | GaussianFittingFunction.eval | @Override
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elki-project/elki | addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/application/greedyensemble/VisualizePairwiseGainMatrix.java | VisualizePairwiseGainMatrix.showVisualization | private void showVisualization(VisualizerContext context, SimilarityMatrixVisualizer factory, VisualizationTask task) {
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elki-project/elki | elki-core-math/src/main/java/de/lmu/ifi/dbs/elki/math/IntegerMinMax.java | IntegerMinMax.put | public void put(int[] data) {
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final int l = data.length;
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elki-project/elki | elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java | KDEOS.run | public OutlierResult run(Database database, Relation<O> rel) {
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elki-project/elki | elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java | KDEOS.estimateDensities | protected void estimateDensities(Relation<O> rel, KNNQuery<O> knnq, final DBIDs ids, WritableDataStore<double[]> densities) {
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final int knum = kmax + 1 - kmin;
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final int dim = dimensionality(rel);
final int knum = kmax + 1 - kmin;
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elki-project/elki | elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java | KDEOS.dimensionality | private int dimensionality(Relation<O> rel) {
// Explicit:
if(idim >= 0) {
return idim;
}
// Cast to vector field relation.
@SuppressWarnings("unchecked")
final Relation<NumberVector> frel = (Relation<NumberVector>) rel;
int dim = RelationUtil.dimensionality(frel);
if(dim < 1) {
... | java | private int dimensionality(Relation<O> rel) {
// Explicit:
if(idim >= 0) {
return idim;
}
// Cast to vector field relation.
@SuppressWarnings("unchecked")
final Relation<NumberVector> frel = (Relation<NumberVector>) rel;
int dim = RelationUtil.dimensionality(frel);
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elki-project/elki | elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/KDEOS.java | KDEOS.computeOutlierScores | protected void computeOutlierScores(KNNQuery<O> knnq, final DBIDs ids, WritableDataStore<double[]> densities, WritableDoubleDataStore kdeos, DoubleMinMax minmax) {
final int knum = kmax + 1 - kmin;
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final int knum = kmax + 1 - kmin;
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Computing KDEOS scores", ids.size(), LOG) : null;
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java | CASH.run | public Clustering<Model> run(Relation<V> rel) {
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noiseDim = dimensionality(fulldatabase);
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fulldatabase = preprocess(rel);
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noiseDim = dimensionality(fulldatabase);
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java | CASH.preprocess | private Relation<ParameterizationFunction> preprocess(Relation<V> vrel) {
DBIDs ids = vrel.getDBIDs();
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DBIDs ids = vrel.getDBIDs();
SimpleTypeInformation<ParameterizationFunction> type = new SimpleTypeInformation<>(ParameterizationFunction.class);
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java | CASH.initHeap | private void initHeap(ObjectHeap<CASHInterval> heap, Relation<ParameterizationFunction> relation, int dim, DBIDs ids) {
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CASHIntervalSplit split = new CASHIntervalSplit(relation, minPts);
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double[] minMax = determineMinMaxDistance(relation, dim... | [
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java | CASH.buildDB | private MaterializedRelation<ParameterizationFunction> buildDB(int dim, double[][] basis, DBIDs ids, Relation<ParameterizationFunction> relation) {
ProxyDatabase proxy = new ProxyDatabase(ids);
SimpleTypeInformation<ParameterizationFunction> type = new SimpleTypeInformation<>(ParameterizationFunction.class);
... | java | private MaterializedRelation<ParameterizationFunction> buildDB(int dim, double[][] basis, DBIDs ids, Relation<ParameterizationFunction> relation) {
ProxyDatabase proxy = new ProxyDatabase(ids);
SimpleTypeInformation<ParameterizationFunction> type = new SimpleTypeInformation<>(ParameterizationFunction.class);
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java | CASH.project | private ParameterizationFunction project(double[][] basis, ParameterizationFunction f) {
// Matrix m = new Matrix(new
// double[][]{f.getPointCoordinates()}).times(basis);
double[] m = transposeTimes(basis, f.getColumnVector());
return new ParameterizationFunction(DoubleVector.wrap(m));
} | java | private ParameterizationFunction project(double[][] basis, ParameterizationFunction f) {
// Matrix m = new Matrix(new
// double[][]{f.getPointCoordinates()}).times(basis);
double[] m = transposeTimes(basis, f.getColumnVector());
return new ParameterizationFunction(DoubleVector.wrap(m));
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java | CASH.determineBasis | private double[][] determineBasis(double[] alpha) {
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final int dim = alpha.length;
// Primary vector:
double[] nn = new double[dim + 1];
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java | CASH.determineNextIntervalAtMaxLevel | private CASHInterval determineNextIntervalAtMaxLevel(ObjectHeap<CASHInterval> heap) {
CASHInterval next = doDetermineNextIntervalAtMaxLevel(heap);
// noise path was chosen
while(next == null) {
if(heap.isEmpty()) {
return null;
}
next = doDetermineNextIntervalAtMaxLevel(heap);
... | java | private CASHInterval determineNextIntervalAtMaxLevel(ObjectHeap<CASHInterval> heap) {
CASHInterval next = doDetermineNextIntervalAtMaxLevel(heap);
// noise path was chosen
while(next == null) {
if(heap.isEmpty()) {
return null;
}
next = doDetermineNextIntervalAtMaxLevel(heap);
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java | CASH.doDetermineNextIntervalAtMaxLevel | private CASHInterval doDetermineNextIntervalAtMaxLevel(ObjectHeap<CASHInterval> heap) {
CASHInterval interval = heap.poll();
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// max level is reached
if(interval.getLevel() >= maxLevel && interval.getMaxSplitDimension() == (dim - 1)) {
... | java | private CASHInterval doDetermineNextIntervalAtMaxLevel(ObjectHeap<CASHInterval> heap) {
CASHInterval interval = heap.poll();
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java | CASH.determineMinMaxDistance | private double[] determineMinMaxDistance(Relation<ParameterizationFunction> relation, int dimensionality) {
double[] min = new double[dimensionality - 1];
double[] max = new double[dimensionality - 1];
Arrays.fill(max, Math.PI);
HyperBoundingBox box = new HyperBoundingBox(min, max);
double d_min = ... | java | private double[] determineMinMaxDistance(Relation<ParameterizationFunction> relation, int dimensionality) {
double[] min = new double[dimensionality - 1];
double[] max = new double[dimensionality - 1];
Arrays.fill(max, Math.PI);
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elki-project/elki | elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/statistics/RankingQualityHistogram.java | RankingQualityHistogram.run | public HistogramResult run(Database database, Relation<O> relation) {
final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction());
final KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, relation.size());
if(LOG.isVerbose()) {
LOG.verbose("Preprocessing ... | java | public HistogramResult run(Database database, Relation<O> relation) {
final DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction());
final KNNQuery<O> knnQuery = database.getKNNQuery(distanceQuery, relation.size());
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/em/EM.java | EM.run | public Clustering<M> run(Database database, Relation<V> relation) {
if(relation.size() == 0) {
throw new IllegalArgumentException("database empty: must contain elements");
}
// initial models
List<? extends EMClusterModel<M>> models = mfactory.buildInitialModels(database, relation, k, SquaredEucli... | java | public Clustering<M> run(Database database, Relation<V> relation) {
if(relation.size() == 0) {
throw new IllegalArgumentException("database empty: must contain elements");
}
// initial models
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/em/EM.java | EM.recomputeCovarianceMatrices | public static void recomputeCovarianceMatrices(Relation<? extends NumberVector> relation, WritableDataStore<double[]> probClusterIGivenX, List<? extends EMClusterModel<?>> models, double prior) {
final int k = models.size();
boolean needsTwoPass = false;
for(EMClusterModel<?> m : models) {
m.beginESte... | java | public static void recomputeCovarianceMatrices(Relation<? extends NumberVector> relation, WritableDataStore<double[]> probClusterIGivenX, List<? extends EMClusterModel<?>> models, double prior) {
final int k = models.size();
boolean needsTwoPass = false;
for(EMClusterModel<?> m : models) {
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elki-project/elki | elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/em/EM.java | EM.assignProbabilitiesToInstances | public static double assignProbabilitiesToInstances(Relation<? extends NumberVector> relation, List<? extends EMClusterModel<?>> models, WritableDataStore<double[]> probClusterIGivenX) {
final int k = models.size();
double emSum = 0.;
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advan... | java | public static double assignProbabilitiesToInstances(Relation<? extends NumberVector> relation, List<? extends EMClusterModel<?>> models, WritableDataStore<double[]> probClusterIGivenX) {
final int k = models.size();
double emSum = 0.;
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elki-project/elki | addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/gui/ResultWindow.java | DynamicMenu.updateVisualizerMenus | protected synchronized void updateVisualizerMenus() {
Projection proj = null;
if(svgCanvas.getPlot() instanceof DetailView) {
PlotItem item = ((DetailView) svgCanvas.getPlot()).getPlotItem();
proj = item.proj;
}
menubar.removeAll();
menubar.add(filemenu);
ResultHierar... | java | protected synchronized void updateVisualizerMenus() {
Projection proj = null;
if(svgCanvas.getPlot() instanceof DetailView) {
PlotItem item = ((DetailView) svgCanvas.getPlot()).getPlotItem();
proj = item.proj;
}
menubar.removeAll();
menubar.add(filemenu);
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elki-project/elki | elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/meta/HiCS.java | HiCS.run | public OutlierResult run(Relation<V> relation) {
final DBIDs ids = relation.getDBIDs();
ArrayList<ArrayDBIDs> subspaceIndex = buildOneDimIndexes(relation);
Set<HiCSSubspace> subspaces = calculateSubspaces(relation, subspaceIndex, rnd.getSingleThreadedRandom());
if(LOG.isVerbose()) {
LOG.verbose(... | java | public OutlierResult run(Relation<V> relation) {
final DBIDs ids = relation.getDBIDs();
ArrayList<ArrayDBIDs> subspaceIndex = buildOneDimIndexes(relation);
Set<HiCSSubspace> subspaces = calculateSubspaces(relation, subspaceIndex, rnd.getSingleThreadedRandom());
if(LOG.isVerbose()) {
LOG.verbose(... | [
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elki-project/elki | elki-outlier/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/meta/HiCS.java | HiCS.buildOneDimIndexes | private ArrayList<ArrayDBIDs> buildOneDimIndexes(Relation<? extends NumberVector> relation) {
final int dim = RelationUtil.dimensionality(relation);
ArrayList<ArrayDBIDs> subspaceIndex = new ArrayList<>(dim + 1);
SortDBIDsBySingleDimension comp = new VectorUtil.SortDBIDsBySingleDimension(relation);
for... | java | private ArrayList<ArrayDBIDs> buildOneDimIndexes(Relation<? extends NumberVector> relation) {
final int dim = RelationUtil.dimensionality(relation);
ArrayList<ArrayDBIDs> subspaceIndex = new ArrayList<>(dim + 1);
SortDBIDsBySingleDimension comp = new VectorUtil.SortDBIDsBySingleDimension(relation);
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elki-project/elki | elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mktab/MkTabTree.java | MkTabTree.max | private double[] max(double[] distances1, double[] distances2) {
if(distances1.length != distances2.length) {
throw new RuntimeException("different lengths!");
}
double[] result = new double[distances1.length];
for(int i = 0; i < distances1.length; i++) {
result[i] = Math.max(distances1[i],... | java | private double[] max(double[] distances1, double[] distances2) {
if(distances1.length != distances2.length) {
throw new RuntimeException("different lengths!");
}
double[] result = new double[distances1.length];
for(int i = 0; i < distances1.length; i++) {
result[i] = Math.max(distances1[i],... | [
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] | b54673327e76198ecd4c8a2a901021f1a9174498 | https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index-mtree/src/main/java/de/lmu/ifi/dbs/elki/index/tree/metrical/mtreevariants/mktrees/mktab/MkTabTree.java#L252-L262 | train |
elki-project/elki | addons/joglvis/src/main/java/de/lmu/ifi/dbs/elki/joglvis/ShaderUtil.java | ShaderUtil.compileShader | public static int compileShader(Class<?> context, GL2 gl, int type, String name) throws ShaderCompilationException {
int prog = -1;
try (InputStream in = context.getResourceAsStream(name)) {
int[] error = new int[1];
String shaderdata = FileUtil.slurp(in);
prog = gl.glCreateShader(type);
... | java | public static int compileShader(Class<?> context, GL2 gl, int type, String name) throws ShaderCompilationException {
int prog = -1;
try (InputStream in = context.getResourceAsStream(name)) {
int[] error = new int[1];
String shaderdata = FileUtil.slurp(in);
prog = gl.glCreateShader(type);
... | [
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] | b54673327e76198ecd4c8a2a901021f1a9174498 | https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/addons/joglvis/src/main/java/de/lmu/ifi/dbs/elki/joglvis/ShaderUtil.java#L54-L81 | train |
elki-project/elki | elki-core-distance/src/main/java/de/lmu/ifi/dbs/elki/distance/distancefunction/timeseries/AbstractEditDistanceFunction.java | AbstractEditDistanceFunction.effectiveBandSize | protected int effectiveBandSize(final int dim1, final int dim2) {
if(bandSize == Double.POSITIVE_INFINITY) {
return (dim1 > dim2) ? dim1 : dim2;
}
if(bandSize >= 1.) {
return (int) bandSize;
}
// Max * bandSize:
return (int) Math.ceil((dim1 >= dim2 ? dim1 : dim2) * bandSize);
} | java | protected int effectiveBandSize(final int dim1, final int dim2) {
if(bandSize == Double.POSITIVE_INFINITY) {
return (dim1 > dim2) ? dim1 : dim2;
}
if(bandSize >= 1.) {
return (int) bandSize;
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// Max * bandSize:
return (int) Math.ceil((dim1 >= dim2 ? dim1 : dim2) * bandSize);
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] | b54673327e76198ecd4c8a2a901021f1a9174498 | https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-core-distance/src/main/java/de/lmu/ifi/dbs/elki/distance/distancefunction/timeseries/AbstractEditDistanceFunction.java#L61-L70 | train |
elki-project/elki | elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java | AbstractNode.addLeafEntry | @Override
public final int addLeafEntry(E entry) {
// entry is not a leaf entry
if(!(entry instanceof LeafEntry)) {
throw new UnsupportedOperationException("Entry is not a leaf entry!");
}
// this is a not a leaf node
if(!isLeaf()) {
throw new UnsupportedOperationException("Node is not... | java | @Override
public final int addLeafEntry(E entry) {
// entry is not a leaf entry
if(!(entry instanceof LeafEntry)) {
throw new UnsupportedOperationException("Entry is not a leaf entry!");
}
// this is a not a leaf node
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elki-project/elki | elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java | AbstractNode.addDirectoryEntry | @Override
public final int addDirectoryEntry(E entry) {
// entry is not a directory entry
if(entry instanceof LeafEntry) {
throw new UnsupportedOperationException("Entry is not a directory entry!");
}
// this is a not a directory node
if(isLeaf()) {
throw new UnsupportedOperationExcept... | java | @Override
public final int addDirectoryEntry(E entry) {
// entry is not a directory entry
if(entry instanceof LeafEntry) {
throw new UnsupportedOperationException("Entry is not a directory entry!");
}
// this is a not a directory node
if(isLeaf()) {
throw new UnsupportedOperationExcept... | [
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elki-project/elki | elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java | AbstractNode.deleteEntry | public boolean deleteEntry(int index) {
System.arraycopy(entries, index + 1, entries, index, numEntries - index - 1);
entries[--numEntries] = null;
return true;
} | java | public boolean deleteEntry(int index) {
System.arraycopy(entries, index + 1, entries, index, numEntries - index - 1);
entries[--numEntries] = null;
return true;
} | [
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@return true id deletion was successful | [
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] | b54673327e76198ecd4c8a2a901021f1a9174498 | https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java#L212-L216 | train |
elki-project/elki | elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java | AbstractNode.getEntries | @SuppressWarnings("unchecked")
@Deprecated
public final List<E> getEntries() {
List<E> result = new ArrayList<>(numEntries);
for(Entry entry : entries) {
if(entry != null) {
result.add((E) entry);
}
}
return result;
} | java | @SuppressWarnings("unchecked")
@Deprecated
public final List<E> getEntries() {
List<E> result = new ArrayList<>(numEntries);
for(Entry entry : entries) {
if(entry != null) {
result.add((E) entry);
}
}
return result;
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] | b54673327e76198ecd4c8a2a901021f1a9174498 | https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java#L245-L255 | train |
elki-project/elki | elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java | AbstractNode.removeMask | public void removeMask(long[] mask) {
int dest = BitsUtil.nextSetBit(mask, 0);
if(dest < 0) {
return;
}
int src = BitsUtil.nextSetBit(mask, dest);
while(src < numEntries) {
if(!BitsUtil.get(mask, src)) {
entries[dest] = entries[src];
dest++;
}
src++;
}
... | java | public void removeMask(long[] mask) {
int dest = BitsUtil.nextSetBit(mask, 0);
if(dest < 0) {
return;
}
int src = BitsUtil.nextSetBit(mask, dest);
while(src < numEntries) {
if(!BitsUtil.get(mask, src)) {
entries[dest] = entries[src];
dest++;
}
src++;
}
... | [
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] | b54673327e76198ecd4c8a2a901021f1a9174498 | https://github.com/elki-project/elki/blob/b54673327e76198ecd4c8a2a901021f1a9174498/elki-index/src/main/java/de/lmu/ifi/dbs/elki/index/tree/AbstractNode.java#L274-L293 | train |
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