code stringlengths 73 34.1k | label stringclasses 1
value |
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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 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));
}
else { // Gumbel case:
return Fas... | 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);
}
else if(k > 0) {
return mu + sigma * Math.min((1. - F... | 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);
} | 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;
}
return sigma * FastMath.sqrt(-2. * FastMath.log(1. - val));
} | 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);
KernelMatrix kernelMatrix = new KernelMatrix(sq, ... | 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;
}
do... | 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());
KNNQuery<O> knnq = dat... | java |
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 |
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 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 void logVarstat(DoubleStatistic varstat, double[] varsum) {
if(varstat != null) {
double s = sum(varsum);
getLogger().statistics(varstat.setDouble(s));
}
} | 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);
for (String str : ... | java |
public void load() throws FileNotFoundException, IOException {
BufferedReader is = new BufferedReader(new InputStreamReader(new FileInputStream(file)));
ArrayList<String> buf = new ArrayList<>();
while (is.ready()) {
String line = is.readLine();
// skip comments
if (line.startsWith(COMMENT... | java |
public void remove(String key) {
Iterator<Pair<String, ArrayList<String>>> it = store.iterator();
while (it.hasNext()) {
String thisKey = it.next().first;
if (key.equals(thisKey)) {
it.remove();
break;
}
}
} | java |
public ArrayList<String> get(String key) {
Iterator<Pair<String, ArrayList<String>>> it = store.iterator();
while (it.hasNext()) {
Pair<String, ArrayList<String>> pair = it.next();
if (key.equals(pair.first)) {
return pair.second;
}
}
return null;
} | java |
public Clustering<Model> run(Database database, Relation<V> relation) {
// current dimensionality associated with each seed
int dim_c = RelationUtil.dimensionality(relation);
if(dim_c < l) {
throw new IllegalStateException("Dimensionality of data < parameter l! " + "(" + dim_c + " < " + l + ")");
... | java |
private List<ORCLUSCluster> initialSeeds(Relation<V> database, int k) {
DBIDs randomSample = DBIDUtil.randomSample(database.getDBIDs(), k, rnd);
List<ORCLUSCluster> seeds = new ArrayList<>(k);
for(DBIDIter iter = randomSample.iter(); iter.valid(); iter.advance()) {
seeds.add(new ORCLUSCluster(database... | java |
private void assign(Relation<V> database, List<ORCLUSCluster> clusters) {
NumberVectorDistanceFunction<? super V> distFunc = SquaredEuclideanDistanceFunction.STATIC;
// clear the current clusters
for(ORCLUSCluster cluster : clusters) {
cluster.objectIDs.clear();
}
// projected centroids of th... | java |
private void merge(Relation<V> relation, List<ORCLUSCluster> clusters, int k_new, int d_new, IndefiniteProgress cprogress) {
ArrayList<ProjectedEnergy> projectedEnergies = new ArrayList<>((clusters.size() * (clusters.size() - 1)) >>> 1);
for(int i = 0; i < clusters.size(); i++) {
for(int j = i + 1; j < cl... | 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);
d... | java |
private ORCLUSCluster union(Relation<V> relation, ORCLUSCluster c1, ORCLUSCluster c2, int dim) {
ORCLUSCluster c = new ORCLUSCluster();
c.objectIDs = DBIDUtil.newHashSet(c1.objectIDs);
c.objectIDs.addDBIDs(c2.objectIDs);
c.objectIDs = DBIDUtil.newArray(c.objectIDs);
if(c.objectIDs.size() > 0) {
... | java |
private static void initializeNNCache(double[] scratch, double[] bestd, int[] besti) {
final int size = bestd.length;
Arrays.fill(bestd, Double.POSITIVE_INFINITY);
Arrays.fill(besti, -1);
for(int x = 0, p = 0; x < size; x++) {
assert (p == MatrixParadigm.triangleSize(x));
double bestdx = Dou... | java |
protected int findMerge(int size, MatrixParadigm mat, double[] bestd, int[] besti, PointerHierarchyRepresentationBuilder builder) {
double mindist = Double.POSITIVE_INFINITY;
int x = -1, y = -1;
// Find minimum:
for(int cx = 0; cx < size; cx++) {
// Skip if object has already joined a cluster:
... | java |
protected void merge(int size, MatrixParadigm mat, double[] bestd, int[] besti, PointerHierarchyRepresentationBuilder builder, double mindist, int x, int y) {
// Avoid allocating memory, by reusing existing iterators:
final DBIDArrayIter ix = mat.ix.seek(x), iy = mat.iy.seek(y);
if(LOG.isDebuggingFine()) {
... | java |
private void updateCache(int size, double[] scratch, double[] bestd, int[] besti, int x, int y, int j, double d) {
// New best
if(d <= bestd[j]) {
bestd[j] = d;
besti[j] = y;
return;
}
// Needs slow update.
if(besti[j] == x || besti[j] == y) {
findBest(size, scratch, bestd, b... | java |
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 static String getTitle(Database db, Result result) {
List<TrackedParameter> settings = new ArrayList<>();
for(SettingsResult sr : SettingsResult.getSettingsResults(result)) {
settings.addAll(sr.getSettings());
}
String algorithm = null;
String distance = null;
String dataset = null;... | java |
protected static String shortenClassname(String nam, char c) {
final int lastdot = nam.lastIndexOf(c);
if(lastdot >= 0) {
nam = nam.substring(lastdot + 1);
}
return nam;
} | java |
private static Class<?> getRestrictionClass(OptionID oid, final Parameter<?> firstopt, Map<OptionID, List<Pair<Parameter<?>, Class<?>>>> byopt) {
Class<?> superclass = getRestrictionClass(firstopt);
// Also look for more general restrictions:
for(Pair<Parameter<?>, Class<?>> clinst : byopt.get(oid)) {
... | java |
private static <T> ArrayList<T> sorted(Collection<T> cls, Comparator<? super T> c) {
ArrayList<T> sorted = new ArrayList<>(cls);
sorted.sort(c);
return sorted;
} | java |
protected void handleHoverEvent(Event evt) {
if(evt.getTarget() instanceof Element) {
Element e = (Element) evt.getTarget();
Node next = e.getNextSibling();
if(next instanceof Element) {
toggleTooltip((Element) next, evt.getType());
}
else {
LoggingUtil.warning("Tooltip... | 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);
... | 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
... | 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());
}
}
else {
for(int i = 0; i < node.getNumEntries();... | java |
private PolynomialApproximation approximateKnnDistances(double[] knnDistances) {
StringBuilder msg = new StringBuilder();
// count the zero distances (necessary of log-log space is used)
int k_0 = 0;
if(settings.log) {
for(int i = 0; i < settings.kmax; i++) {
double dist = knnDistances[i]... | 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... | java |
private double mdist(double[] a, double[] b) {
return Math.abs(a[0] - b[0]) + Math.abs(a[1] - b[1]);
} | java |
private boolean isConvex(double[] a, double[] b, double[] c) {
// 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]);
return (-1e-13 < area && area < 1e-13) ? (mdist(b, c) > mdist(a, b) + mdist(a,... | 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()) {
doub... | java |
public Polygon getHull() {
if(!ok) {
computeConvexHull();
}
return new Polygon(points, minmaxX.getMin(), minmaxX.getMax(), minmaxY.getMin(), minmaxY.getMax());
} | java |
private static double coverRadius(double[][] matrix, int[] idx, int i) {
final int idx_i = idx[i];
final double[] row_i = matrix[i];
double m = 0;
for(int j = 0; j < row_i.length; j++) {
if(i != j && idx_i == idx[j]) {
final double d = row_i[j];
m = d > m ? d : m;
}
}
... | java |
private static int[] mstPartition(double[][] matrix) {
final int n = matrix.length;
int[] edges = PrimsMinimumSpanningTree.processDense(matrix);
// Note: Prims does *not* yield edges sorted by edge length!
double meanlength = thresholdLength(matrix, edges);
int[] idx = new int[n], best = new int[n],... | java |
private static double thresholdLength(double[][] matrix, int[] edges) {
double[] lengths = new double[edges.length >> 1];
for(int i = 0, e = edges.length - 1; i < e; i += 2) {
lengths[i >> 1] = matrix[edges[i]][edges[i + 1]];
}
Arrays.sort(lengths);
final int pos = (lengths.length >> 1); // 50... | java |
private static double edgelength(double[][] matrix, int[] edges, int i) {
i <<= 1;
return matrix[edges[i]][edges[i + 1]];
} | java |
private static void omitEdge(int[] edges, int[] idx, int[] sizes, int omit) {
for(int i = 0; i < idx.length; i++) {
idx[i] = i;
}
Arrays.fill(sizes, 1);
for(int i = 0, j = 0, e = edges.length - 1; j < e; i++, j += 2) {
if(i == omit) {
continue;
}
int ea = edges[j + 1], eb... | java |
private static int follow(int i, int[] partitions) {
int next = partitions[i], tmp;
while(i != next) {
tmp = next;
next = partitions[i] = partitions[next];
i = tmp;
}
return 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()) {
NumberVector v = relation.get(it);
for(int i = 0; i < dim; i++) {
... | java |
public static <O> DistanceQuery<O> getDistanceQuery(Database database, DistanceFunction<? super O> distanceFunction, Object... hints) {
final Relation<O> objectQuery = database.getRelation(distanceFunction.getInputTypeRestriction(), hints);
return database.getDistanceQuery(objectQuery, distanceFunction, hints);... | java |
public static <O> SimilarityQuery<O> getSimilarityQuery(Database database, SimilarityFunction<? super O> similarityFunction, Object... hints) {
final Relation<O> objectQuery = database.getRelation(similarityFunction.getInputTypeRestriction(), hints);
return database.getSimilarityQuery(objectQuery, similarityFun... | java |
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);
} | java |
public static <O> RangeQuery<O> getLinearScanSimilarityRangeQuery(SimilarityQuery<O> simQuery) {
// Slight optimizations of linear scans
if(simQuery instanceof PrimitiveSimilarityQuery) {
final PrimitiveSimilarityQuery<O> pdq = (PrimitiveSimilarityQuery<O>) simQuery;
return new LinearScanPrimitiveSi... | java |
protected static void register(Class<?> parent, String cname) {
Entry e = data.get(parent);
if(e == null) {
data.put(parent, e = new Entry());
}
e.addName(cname);
} | java |
protected static void register(Class<?> parent, Class<?> clazz) {
Entry e = data.get(parent);
if(e == null) {
data.put(parent, e = new Entry());
}
final String cname = clazz.getCanonicalName();
e.addHit(cname, clazz);
if(clazz.isAnnotationPresent(Alias.class)) {
Alias aliases = clazz... | java |
protected static void registerAlias(Class<?> parent, String alias, String cname) {
Entry e = data.get(parent);
assert (e != null);
e.addAlias(alias, cname);
} | java |
private static Class<?> tryLoadClass(String value) {
try {
return CLASSLOADER.loadClass(value);
}
catch(ClassNotFoundException e) {
return null;
}
} | java |
public static List<Class<?>> findAllImplementations(Class<?> restrictionClass) {
if(restrictionClass == null) {
return Collections.emptyList();
}
if(!contains(restrictionClass)) {
ELKIServiceLoader.load(restrictionClass);
ELKIServiceScanner.load(restrictionClass);
}
Entry e = data.... | java |
public static List<Class<?>> findAllImplementations(Class<?> c, boolean everything, boolean parameterizable) {
if(c == null) {
return Collections.emptyList();
}
// Default is served from the registry
if(!everything && parameterizable) {
return findAllImplementations(c);
}
// This cod... | java |
private static <C> Class<?> tryAlternateNames(Class<? super C> restrictionClass, String value, Entry e) {
StringBuilder buf = new StringBuilder(value.length() + 100);
// Try with FACTORY_POSTFIX first:
Class<?> clazz = tryLoadClass(buf.append(value).append(FACTORY_POSTFIX).toString());
if(clazz != null)... | java |
protected Element setupCanvas() {
final double margin = context.getStyleLibrary().getSize(StyleLibrary.MARGIN);
this.layer = setupCanvas(svgp, this.proj, margin, getWidth(), getHeight());
return layer;
} | java |
protected SimpleTypeInformation<?> convertedType(SimpleTypeInformation<?> in, NumberVector.Factory<V> factory) {
return new VectorFieldTypeInformation<>(factory, tdim);
} | java |
protected <O> Map<O, IntList> partition(List<? extends O> classcolumn) {
Map<O, IntList> classes = new HashMap<>();
Iterator<? extends O> iter = classcolumn.iterator();
for(int i = 0; iter.hasNext(); i++) {
O lbl = iter.next();
IntList ids = classes.get(lbl);
if(ids == null) {
ids ... | java |
public Curve makeCurve() {
Curve c = new Curve(curves.size());
curves.add(c);
return c;
} | java |
public void publish(String message, Level level) {
try {
publish(new LogRecord(level, message));
}
catch(BadLocationException e) {
throw new RuntimeException("Error writing a log-like message.", e);
}
} | java |
protected synchronized void publish(LogRecord record) throws BadLocationException {
// choose an appropriate formatter
final Formatter fmt;
final Style style;
// always format progress messages using the progress formatter.
if(record.getLevel().intValue() >= Level.WARNING.intValue()) {
// form... | java |
protected void optimizeSNE(AffinityMatrix pij, double[][] sol) {
final int size = pij.size();
if(size * 3L * dim > 0x7FFF_FFFAL) {
throw new AbortException("Memory exceeds Java array size limit.");
}
// Meta information on each point; joined for memory locality.
// Gradient, Momentum, and lear... | java |
protected double computeQij(double[][] qij, double[][] solution) {
double qij_sum = 0;
for(int i = 1; i < qij.length; i++) {
final double[] qij_i = qij[i], vi = solution[i];
for(int j = 0; j < i; j++) {
qij_sum += qij_i[j] = qij[j][i] = MathUtil.exp(-sqDist(vi, solution[j]));
}
}
... | java |
protected void computeGradient(AffinityMatrix pij, double[][] qij, double qij_isum, double[][] sol, double[] meta) {
final int dim3 = dim * 3;
int size = pij.size();
for(int i = 0, off = 0; i < size; i++, off += dim3) {
final double[] sol_i = sol[i], qij_i = qij[i];
Arrays.fill(meta, off, off + ... | java |
public OutlierResult run(Database database, Relation<O> relation) {
DistanceFunction<? super O> df = clusterer.getDistanceFunction();
DistanceQuery<O> dq = database.getDistanceQuery(relation, df);
// TODO: improve ELKI api to ensure we're using the same DBIDs!
Clustering<?> c = clusterer.run(database, ... | java |
@Override
public FittingFunctionResult eval(double x, double[] params) {
final int len = params.length;
// We always need triples: (mean, stddev, scaling)
assert (len % 3) == 0;
double y = 0.0;
double[] gradients = new double[len];
// Loosely based on the book:
// Numerical Recipes in C:... | java |
private void showVisualization(VisualizerContext context, SimilarityMatrixVisualizer factory, VisualizationTask task) {
VisualizationPlot plot = new VisualizationPlot();
Visualization vis = factory.makeVisualization(context, task, plot, 1.0, 1.0, null);
plot.getRoot().appendChild(vis.getLayer());
plot.g... | java |
public void put(int[] data) {
final int l = data.length;
for(int i = 0; i < l; i++) {
put(data[i]);
}
} | java |
public OutlierResult run(Database database, Relation<O> rel) {
final DBIDs ids = rel.getDBIDs();
LOG.verbose("Running kNN preprocessor.");
KNNQuery<O> knnq = DatabaseUtil.precomputedKNNQuery(database, rel, getDistanceFunction(), kmax + 1);
// Initialize store for densities
WritableDataStore<double... | java |
protected void estimateDensities(Relation<O> rel, KNNQuery<O> knnq, final DBIDs ids, WritableDataStore<double[]> densities) {
final int dim = dimensionality(rel);
final int knum = kmax + 1 - kmin;
// Initialize storage:
for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
densities.put(... | 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);
if(dim < 1) {
... | java |
protected void computeOutlierScores(KNNQuery<O> knnq, final DBIDs ids, WritableDataStore<double[]> densities, WritableDoubleDataStore kdeos, DoubleMinMax minmax) {
final int knum = kmax + 1 - kmin;
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Computing KDEOS scores", ids.size(), LOG) : null;
... | java |
public Clustering<Model> run(Relation<V> rel) {
fulldatabase = preprocess(rel);
processedIDs = DBIDUtil.newHashSet(fulldatabase.size());
noiseDim = dimensionality(fulldatabase);
FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("CASH Clustering", fulldatabase.size(), LOG) : null;
Clust... | java |
private Relation<ParameterizationFunction> preprocess(Relation<V> vrel) {
DBIDs ids = vrel.getDBIDs();
SimpleTypeInformation<ParameterizationFunction> type = new SimpleTypeInformation<>(ParameterizationFunction.class);
WritableDataStore<ParameterizationFunction> prep = DataStoreUtil.makeStorage(ids, DataSto... | java |
private void initHeap(ObjectHeap<CASHInterval> heap, Relation<ParameterizationFunction> relation, int dim, DBIDs ids) {
CASHIntervalSplit split = new CASHIntervalSplit(relation, minPts);
// determine minimum and maximum function value of all functions
double[] minMax = determineMinMaxDistance(relation, dim... | 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);
... | 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));
} | java |
private double[][] determineBasis(double[] alpha) {
final int dim = alpha.length;
// Primary vector:
double[] nn = new double[dim + 1];
for(int i = 0; i < nn.length; i++) {
double alpha_i = i == alpha.length ? 0 : alpha[i];
nn[i] = ParameterizationFunction.sinusProduct(0, i, alpha) * FastMat... | 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);
... | java |
private CASHInterval doDetermineNextIntervalAtMaxLevel(ObjectHeap<CASHInterval> heap) {
CASHInterval interval = heap.poll();
int dim = interval.getDimensionality();
while(true) {
// max level is reached
if(interval.getLevel() >= maxLevel && interval.getMaxSplitDimension() == (dim - 1)) {
... | 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);
HyperBoundingBox box = new HyperBoundingBox(min, max);
double d_min = ... | 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());
if(LOG.isVerbose()) {
LOG.verbose("Preprocessing ... | java |
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 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 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 |
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 |
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 |
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 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 |
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 |
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 |
@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 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 |
public boolean deleteEntry(int index) {
System.arraycopy(entries, index + 1, entries, index, numEntries - index - 1);
entries[--numEntries] = null;
return true;
} | 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;
} | 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++;
}
... | java |
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