code stringlengths 73 34.1k | label stringclasses 1
value |
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public static double logcdf(double val, double shape1, double shape2) {
if(val == Double.NEGATIVE_INFINITY) {
return Double.NEGATIVE_INFINITY;
}
if(val == Double.POSITIVE_INFINITY) {
return 0.;
}
if(val != val) {
return Double.NaN;
}
if(shape1 == 0.) {
val = FastMath.... | java |
private double naiveQuerySparse(SparseNumberVector obj, WritableDoubleDataStore scores, HashSetModifiableDBIDs cands) {
double len = 0.; // Length of query object, for final normalization
for(int iter = obj.iter(); obj.iterValid(iter); iter = obj.iterAdvance(iter)) {
final int dim = obj.iterDim(iter);
... | java |
private double naiveQueryDense(NumberVector obj, WritableDoubleDataStore scores, HashSetModifiableDBIDs cands) {
double len = 0.; // Length of query object, for final normalization
for(int dim = 0, max = obj.getDimensionality(); dim < max; dim++) {
final double val = obj.doubleValue(dim);
if(val == ... | java |
private double naiveQuery(V obj, WritableDoubleDataStore scores, HashSetModifiableDBIDs cands) {
if(obj instanceof SparseNumberVector) {
return naiveQuerySparse((SparseNumberVector) obj, scores, cands);
}
else {
return naiveQueryDense(obj, scores, cands);
}
} | java |
protected BundleStreamSource invokeStreamFilters(BundleStreamSource stream) {
assert (stream != null);
if(filters == null) {
return stream;
}
// We dynamically switch between streaming and bundle operations.
MultipleObjectsBundle bundle = null;
for(ObjectFilter filter : filters) {
if... | java |
private void inferCallerELKI() {
needToInferCaller = false;
StackTraceElement[] stack = (new Throwable()).getStackTrace();
int ix = 0;
// skip back to the logger.
while(ix < stack.length) {
StackTraceElement frame = stack[ix];
final String cls = frame.getClassName();
if(cls.equals(... | java |
public static SamplingResult getSamplingResult(final Relation<?> rel) {
Collection<SamplingResult> selections = ResultUtil.filterResults(rel.getHierarchy(), rel, SamplingResult.class);
if(selections.isEmpty()) {
final SamplingResult newsam = new SamplingResult(rel);
ResultUtil.addChildResult(rel, ne... | java |
public Element render(SVGPlot svgp) {
Element tag = svgp.svgElement(SVGConstants.SVG_G_TAG);
Element button = svgp.svgRect(x, y, w, h);
if(!Double.isNaN(r)) {
SVGUtil.setAtt(button, SVGConstants.SVG_RX_ATTRIBUTE, r);
SVGUtil.setAtt(button, SVGConstants.SVG_RY_ATTRIBUTE, r);
}
SVGUtil.set... | java |
public void setTitle(String title, String textcolor) {
this.title = title;
if(titlecss == null) {
titlecss = new CSSClass(this, "text");
titlecss.setStatement(SVGConstants.CSS_TEXT_ANCHOR_PROPERTY, SVGConstants.CSS_MIDDLE_VALUE);
titlecss.setStatement(SVGConstants.CSS_FILL_PROPERTY, textcolor)... | java |
private Pair<PlotItem, VisualizationTask> key(PlotItem item, VisualizationTask task) {
return new Pair<>(item, task);
} | java |
private Pair<Element, Visualization> value(Element elem, Visualization vis) {
return new Pair<>(elem, vis);
} | java |
public void put(PlotItem it, VisualizationTask task, Element elem, Visualization vis) {
map.put(key(it, task), value(elem, vis));
} | java |
public Pair<Element, Visualization> remove(PlotItem it, VisualizationTask task) {
return map.remove(key(it, task));
} | java |
public void put(PlotItem it, VisualizationTask task, Pair<Element, Visualization> pair) {
map.put(key(it, task), pair);
} | java |
public double coveringRadiusFromEntries(DBID routingObjectID, AbstractMTree<O, N, E, ?> mTree) {
double coveringRadius = 0.;
for(int i = 0; i < getNumEntries(); i++) {
E entry = getEntry(i);
final double cover = entry.getParentDistance() + entry.getCoveringRadius();
coveringRadius = coveringRa... | java |
public static double quadraticEuclidean(double[] v1, double[] v2) {
final double d1 = v1[0] - v2[0], d2 = v1[1] - v2[1];
return (d1 * d1) + (d2 * d2);
} | java |
protected void aggregateSpecial(T value, int bin) {
final T exist = getSpecial(bin);
// Note: do not inline above accessor, as getSpecial will initialize the
// special variable used below!
special[bin] = aggregate(exist, value);
} | java |
protected void removePreviousRelation(Relation<?> relation) {
if(keep) {
return;
}
boolean first = true;
for(It<Index> it = relation.getHierarchy().iterDescendants(relation).filter(Index.class); it.valid(); it.advance()) {
if(first) {
Logging.getLogger(getClass()).statistics("Index s... | java |
protected double[] kNNDistances() {
int k = getEntry(0).getKnnDistances().length;
double[] result = new double[k];
for(int i = 0; i < getNumEntries(); i++) {
for(int j = 0; j < k; j++) {
MkTabEntry entry = getEntry(i);
result[j] = Math.max(result[j], entry.getKnnDistance(j + 1));
... | java |
public OutlierResult run(Database database, Relation<O> relation) {
StepProgress stepprog = LOG.isVerbose() ? new StepProgress("VOV", 3) : null;
DBIDs ids = relation.getDBIDs();
int dim = RelationUtil.dimensionality(relation);
LOG.beginStep(stepprog, 1, "Materializing nearest-neighbor sets.");
KNNQ... | java |
private void computeVOVs(KNNQuery<O> knnq, DBIDs ids, DoubleDataStore vols, WritableDoubleDataStore vovs, DoubleMinMax vovminmax) {
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Variance of Volume", ids.size(), LOG) : null;
boolean warned = false;
for(DBIDIter iter = ids.iter(); iter.valid(); ... | java |
private void boundSize(HashSetModifiableDBIDs set, int items) {
if(set.size() > items) {
DBIDs sample = DBIDUtil.randomSample(set, items, rnd);
set.clear();
set.addDBIDs(sample);
}
} | java |
private boolean add(DBIDRef cur, DBIDRef cand, double distance) {
KNNHeap neighbors = store.get(cur);
if(neighbors.contains(cand)) {
return false;
}
double newKDistance = neighbors.insert(distance, cand);
return (distance <= newKDistance);
} | java |
private int sampleNew(DBIDs ids, WritableDataStore<HashSetModifiableDBIDs> sampleNewNeighbors, WritableDataStore<HashSetModifiableDBIDs> newNeighborHash, int items) {
int t = 0;
for(DBIDIter iditer = ids.iter(); iditer.valid(); iditer.advance()) {
KNNHeap realNeighbors = store.get(iditer);
HashSetMo... | java |
private void reverse(WritableDataStore<HashSetModifiableDBIDs> sampleNewHash, WritableDataStore<HashSetModifiableDBIDs> newReverseNeighbors, WritableDataStore<HashSetModifiableDBIDs> oldReverseNeighbors) {
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
KNNHeap heap = store.get... | java |
public static double similarityNumberVector(NumberVector o1, NumberVector o2) {
final int d1 = o1.getDimensionality(), d2 = o2.getDimensionality();
int intersection = 0, union = 0;
int d = 0;
for(; d < d1 && d < d2; d++) {
double v1 = o1.doubleValue(d), v2 = o2.doubleValue(d);
if(v1 != v1 ||... | java |
@Deprecated
protected final Map<DBID, KNNList> batchNN(N node, DBIDs ids, int kmax) {
Map<DBID, KNNList> res = new HashMap<>(ids.size());
for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
DBID id = DBIDUtil.deref(iter);
res.put(id, knnq.getKNNForDBID(id, kmax));
}
return res;... | java |
void writeResult(PrintStream out, DBIDs ids, OutlierResult result, ScalingFunction scaling, String label) {
if(scaling instanceof OutlierScaling) {
((OutlierScaling) scaling).prepare(result);
}
out.append(label);
DoubleRelation scores = result.getScores();
for(DBIDIter iter = ids.iter(); iter.... | java |
private void runForEachK(String prefix, int mink, int maxk, IntFunction<OutlierResult> runner, BiConsumer<String, OutlierResult> out) {
if(isDisabled(prefix)) {
LOG.verbose("Skipping (disabled): " + prefix);
return; // Disabled
}
LOG.verbose("Running " + prefix);
final int digits = (int) Fas... | java |
public double[] getCoefficients() {
double[] result = new double[b.length];
System.arraycopy(b, 0, result, 0, b.length);
return result;
} | java |
public double getValueAt(int k) {
double result = 0.;
double log_k = FastMath.log(k), acc = 1.;
for (int p = 0; p < b.length; p++) {
result += b[p] * acc;
acc *= log_k;
}
return result;
} | java |
@SuppressWarnings("unchecked")
private static <V extends FeatureVector<F>, F> ArrayAdapter<F, ? super V> getAdapter(Factory<V, F> factory) {
if(factory instanceof NumberVector.Factory) {
return (ArrayAdapter<F, ? super V>) NumberVectorAdapter.STATIC;
}
return (ArrayAdapter<F, ? super V>) FeatureVect... | java |
protected void expandClusterOrder(DBID ipt, ClusterOrder order, DistanceQuery<V> dq, FiniteProgress prog) {
UpdatableHeap<OPTICSHeapEntry> heap = new UpdatableHeap<>();
heap.add(new OPTICSHeapEntry(ipt, null, Double.POSITIVE_INFINITY));
while(!heap.isEmpty()) {
final OPTICSHeapEntry current = heap.pol... | java |
public synchronized void resizeMatrix(int newsize) throws IOException {
if(newsize >= 0xFFFF) {
throw new RuntimeException("Matrix size is too big and will overflow the integer datatype.");
}
if(!array.isWritable()) {
throw new IOException("Can't resize a read-only array.");
}
array.resi... | java |
private int computeOffset(int x, int y) {
if(y > x) {
return computeOffset(y, x);
}
return ((x * (x + 1)) >> 1) + y;
} | java |
private void validateHeader(boolean validateRecordSize) throws IOException {
int readmagic = file.readInt();
// Validate magic number
if (readmagic != this.magic) {
file.close();
throw new IOException("Magic in LinearDiskCache does not match: " + readmagic + " instead of " + this.magic);
}
... | java |
public synchronized void resizeFile(int newsize) throws IOException {
if (!writable) {
throw new IOException("File is not writeable!");
}
// update the number of records
this.numrecs = newsize;
file.seek(HEADER_POS_SIZE);
file.writeInt(numrecs);
// resize file
file.setLength(index... | java |
public synchronized ByteBuffer getExtraHeader() throws IOException {
final int size = headersize - INTERNAL_HEADER_SIZE;
final MapMode mode = writable ? MapMode.READ_WRITE : MapMode.READ_ONLY;
return file.getChannel().map(mode, INTERNAL_HEADER_SIZE, size);
} | java |
public PointerPrototypeHierarchyRepresentationResult run(Database db, Relation<O> relation) {
DistanceQuery<O> dq = DatabaseUtil.precomputedDistanceQuery(db, relation, getDistanceFunction(), LOG);
final DBIDs ids = relation.getDBIDs();
final int size = ids.size();
// Initialize space for result:
Po... | java |
protected static <O> void initializeMatrices(MatrixParadigm mat, ArrayModifiableDBIDs prots, DistanceQuery<O> dq) {
final DBIDArrayIter ix = mat.ix, iy = mat.iy;
final double[] distances = mat.matrix;
int pos = 0;
for(ix.seek(0); ix.valid(); ix.advance()) {
for(iy.seek(0); iy.getOffset() < ix.getO... | java |
protected static int findMerge(int end, MatrixParadigm mat, DBIDArrayMIter prots, PointerHierarchyRepresentationBuilder builder, Int2ObjectOpenHashMap<ModifiableDBIDs> clusters, DistanceQuery<?> dq) {
final DBIDArrayIter ix = mat.ix, iy = mat.iy;
final double[] distances = mat.matrix;
double mindist = Doubl... | java |
protected static void merge(int size, MatrixParadigm mat, DBIDArrayMIter prots, PointerHierarchyRepresentationBuilder builder, Int2ObjectOpenHashMap<ModifiableDBIDs> clusters, DistanceQuery<?> dq, int x, int y) {
assert (y < x);
final DBIDArrayIter ix = mat.ix.seek(x), iy = mat.iy.seek(y);
final double[] di... | java |
protected static <O> void updateMatrices(int size, MatrixParadigm mat, DBIDArrayMIter prots, PointerHierarchyRepresentationBuilder builder, Int2ObjectOpenHashMap<ModifiableDBIDs> clusters, DistanceQuery<O> dq, int c) {
final DBIDArrayIter ix = mat.ix, iy = mat.iy;
// c is the new cluster.
// Update entries ... | java |
protected static void updateEntry(MatrixParadigm mat, DBIDArrayMIter prots, Int2ObjectOpenHashMap<ModifiableDBIDs> clusters, DistanceQuery<?> dq, int x, int y) {
assert (y < x);
final DBIDArrayIter ix = mat.ix, iy = mat.iy;
final double[] distances = mat.matrix;
ModifiableDBIDs cx = clusters.get(x), cy ... | java |
private static double findMax(DistanceQuery<?> dq, DBIDIter i, DBIDs cy, double maxDist, double minMaxDist) {
for(DBIDIter j = cy.iter(); j.valid(); j.advance()) {
double dist = dq.distance(i, j);
if(dist > maxDist) {
// Stop early, if we already know a better candidate.
if(dist >= minMa... | java |
@Override
public void writeExternal(ObjectOutput out) throws IOException {
out.writeInt(DBIDUtil.asInteger(id));
out.writeInt(values.length);
for(double v : values) {
out.writeDouble(v);
}
} | java |
@Override
public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {
id = DBIDUtil.importInteger(in.read());
values = new double[in.readInt()];
for(int d = 0; d < values.length; d++) {
values[d] = in.readDouble();
}
} | java |
@Override
public StringBuilder appendToBuffer(StringBuilder buf) {
buf.append(getTask());
buf.append(": ");
buf.append(getProcessed());
return buf;
} | java |
private TypeInformation getInputTypeRestriction() {
// Find maximum dimension requested
int m = dims[0];
for(int i = 1; i < dims.length; i++) {
m = Math.max(dims[i], m);
}
return VectorFieldTypeInformation.typeRequest(NumberVector.class, m, Integer.MAX_VALUE);
} | java |
private boolean isLocalMaximum(double kdist, DBIDs neighbors, WritableDoubleDataStore kdists) {
for(DBIDIter it = neighbors.iter(); it.valid(); it.advance()) {
if(kdists.doubleValue(it) < kdist) {
return false;
}
}
return true;
} | java |
protected int expandCluster(final int clusterid, final WritableIntegerDataStore clusterids, final KNNQuery<O> knnq, final DBIDs neighbors, final double maxkdist, final FiniteProgress progress) {
int clustersize = 1; // initial seed!
final ArrayModifiableDBIDs activeSet = DBIDUtil.newArray();
activeSet.addDB... | java |
private void fillDensities(KNNQuery<O> knnq, DBIDs ids, WritableDoubleDataStore dens) {
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Densities", ids.size(), LOG) : null;
for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
final KNNList neighbors = knnq.getKNNForDBID(iter, k);
... | java |
public Clustering<SubspaceModel> run(Relation<? extends NumberVector> relation) {
final int dimensionality = RelationUtil.dimensionality(relation);
StepProgress step = new StepProgress(2);
// 1. Identification of subspaces that contain clusters
step.beginStep(1, "Identification of subspaces that contai... | java |
private List<Pair<Subspace, ModifiableDBIDs>> determineClusters(List<CLIQUESubspace> denseSubspaces) {
List<Pair<Subspace, ModifiableDBIDs>> clusters = new ArrayList<>();
for(CLIQUESubspace subspace : denseSubspaces) {
List<Pair<Subspace, ModifiableDBIDs>> clustersInSubspace = subspace.determineClusters(... | java |
private List<CLIQUESubspace> findOneDimensionalDenseSubspaces(Relation<? extends NumberVector> database) {
List<CLIQUESubspace> denseSubspaceCandidates = findOneDimensionalDenseSubspaceCandidates(database);
return prune ? pruneDenseSubspaces(denseSubspaceCandidates) : denseSubspaceCandidates;
} | java |
private void updateMinMax(NumberVector featureVector, double[] minima, double[] maxima) {
assert (minima.length == featureVector.getDimensionality());
for(int d = 0; d < featureVector.getDimensionality(); d++) {
double v = featureVector.doubleValue(d);
if(v == v) { // Avoid NaN.
maxima[d] = ... | java |
private List<CLIQUESubspace> findOneDimensionalDenseSubspaceCandidates(Relation<? extends NumberVector> database) {
Collection<CLIQUEUnit> units = initOneDimensionalUnits(database);
// identify dense units
double total = database.size();
for(DBIDIter it = database.iterDBIDs(); it.valid(); it.advance()) ... | java |
private List<CLIQUESubspace> pruneDenseSubspaces(List<CLIQUESubspace> denseSubspaces) {
int[][] means = computeMeans(denseSubspaces);
double[][] diffs = computeDiffs(denseSubspaces, means[0], means[1]);
double[] codeLength = new double[denseSubspaces.size()];
double minCL = Double.MAX_VALUE;
int min... | java |
private int[][] computeMeans(List<CLIQUESubspace> denseSubspaces) {
int n = denseSubspaces.size() - 1;
int[] mi = new int[n + 1], mp = new int[n + 1];
double resultMI = 0, resultMP = 0;
for(int i = 0; i < denseSubspaces.size(); i++) {
resultMI += denseSubspaces.get(i).getCoverage();
result... | java |
private double[][] computeDiffs(List<CLIQUESubspace> denseSubspaces, int[] mi, int[] mp) {
int n = denseSubspaces.size() - 1;
double[] diff_mi = new double[n + 1], diff_mp = new double[n + 1];
double resultMI = 0, resultMP = 0;
for(int i = 0; i < denseSubspaces.size(); i++) {
double diffMI = Mat... | java |
public void append(SimpleTypeInformation<?> meta, Object data) {
this.meta.add(meta);
this.contents.add(data);
} | java |
public boolean contains(long[] bitset) {
for(int i = 0; i < bitset.length; i++) {
final long b = bitset[i];
if(i >= bits.length && b != 0L) {
return false;
}
if((b & bits[i]) != b) {
return false;
}
}
return true;
} | java |
public double jaccardSimilarity(BitVector v2) {
return BitsUtil.intersectionSize(bits, v2.bits) / (double) BitsUtil.unionSize(bits, v2.bits);
} | java |
public static int writeShort(byte[] array, int offset, int v) {
array[offset + 0] = (byte) (v >>> 8);
array[offset + 1] = (byte) (v >>> 0);
return SIZE_SHORT;
} | java |
public static int writeInt(byte[] array, int offset, int v) {
array[offset + 0] = (byte) (v >>> 24);
array[offset + 1] = (byte) (v >>> 16);
array[offset + 2] = (byte) (v >>> 8);
array[offset + 3] = (byte) (v >>> 0);
return SIZE_INT;
} | java |
public static int writeLong(byte[] array, int offset, long v) {
array[offset + 0] = (byte) (v >>> 56);
array[offset + 1] = (byte) (v >>> 48);
array[offset + 2] = (byte) (v >>> 40);
array[offset + 3] = (byte) (v >>> 32);
array[offset + 4] = (byte) (v >>> 24);
array[offset + 5] = (byte) (v >>> 16)... | java |
public static int writeFloat(byte[] array, int offset, float v) {
return writeInt(array, offset, Float.floatToIntBits(v));
} | java |
public static int writeDouble(byte[] array, int offset, double v) {
return writeLong(array, offset, Double.doubleToLongBits(v));
} | java |
public static short readShort(byte[] array, int offset) {
// First make integers to resolve signed vs. unsigned issues.
int b0 = array[offset + 0] & 0xFF;
int b1 = array[offset + 1] & 0xFF;
return (short) ((b0 << 8) + (b1 << 0));
} | java |
public static int readUnsignedShort(byte[] array, int offset) {
// First make integers to resolve signed vs. unsigned issues.
int b0 = array[offset + 0] & 0xFF;
int b1 = array[offset + 1] & 0xFF;
return ((b0 << 8) + (b1 << 0));
} | java |
public static int readInt(byte[] array, int offset) {
// First make integers to resolve signed vs. unsigned issues.
int b0 = array[offset + 0] & 0xFF;
int b1 = array[offset + 1] & 0xFF;
int b2 = array[offset + 2] & 0xFF;
int b3 = array[offset + 3] & 0xFF;
return ((b0 << 24) + (b1 << 16) + (b2 <<... | java |
public static long readLong(byte[] array, int offset) {
// First make integers to resolve signed vs. unsigned issues.
long b0 = array[offset + 0];
long b1 = array[offset + 1] & 0xFF;
long b2 = array[offset + 2] & 0xFF;
long b3 = array[offset + 3] & 0xFF;
long b4 = array[offset + 4] & 0xFF;
i... | java |
public static void writeUnsignedVarint(ByteBuffer buffer, int val) {
// Extra bytes have the high bit set
while((val & 0x7F) != val) {
buffer.put((byte) ((val & 0x7F) | 0x80));
val >>>= 7;
}
// Last byte doesn't have high bit set
buffer.put((byte) (val & 0x7F));
} | java |
public static void writeUnsignedVarintLong(ByteBuffer buffer, long val) {
// Extra bytes have the high bit set
while((val & 0x7F) != val) {
buffer.put((byte) ((val & 0x7F) | 0x80));
val >>>= 7;
}
// Last byte doesn't have high bit set
buffer.put((byte) (val & 0x7F));
} | java |
public static void writeString(ByteBuffer buffer, String s) throws IOException {
if(s == null) {
s = ""; // Which will be written as Varint 0 = single byte 0.
}
ByteArrayUtil.STRING_SERIALIZER.toByteBuffer(buffer, s);
} | java |
public static int readUnsignedVarint(ByteBuffer buffer) throws IOException {
int val = 0;
int bits = 0;
while(true) {
final int data = buffer.get();
val |= (data & 0x7F) << bits;
if((data & 0x80) == 0) {
return val;
}
bits += 7;
if(bits > 35) {
throw new I... | java |
public static void unmapByteBuffer(final MappedByteBuffer map) {
if(map == null) {
return;
}
map.force();
try {
if(Runtime.class.getDeclaredMethod("version") != null)
return; // At later Java, the hack below will not work anymore.
}
catch(NoSuchMethodException e) {
// T... | java |
private void sortAxes() {
for(int d = 0; d < shared.dim; d++) {
double dist = shared.camera.squaredDistanceFromCamera(shared.layout.getNode(d).getX(), shared.layout.getNode(d).getY());
axes[d].first = -dist;
axes[d].second = d;
}
Arrays.sort(axes);
for(int i = 0; i < shared.dim; i++) {... | java |
private IntIntPair[] sortEdges(int[] dindex) {
IntIntPair[] edgesort = new IntIntPair[shared.layout.edges.size()];
int e = 0;
for(Layout.Edge edge : shared.layout.edges) {
int i1 = dindex[edge.dim1], i2 = dindex[edge.dim2];
edgesort[e] = new IntIntPair(Math.min(i1, i2), e);
e++;
}
... | java |
@Override
public void finalizeFirstPassE() {
double s = 1. / wsum;
for(int i = 0; i < mean.length; i++) {
mean[i] *= s;
}
} | java |
private double restore(int d, double val) {
d = (mean.length == 1) ? 0 : d;
return val * mean[d];
} | java |
public OutlierResult run(Relation<? extends NumberVector> relation) {
final DBIDs ids = relation.getDBIDs();
WritableDoubleDataStore ranks = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_STATIC);
DoubleMinMax minmax = new DoubleMinMax();
KernelDensityEstimator kernel = new KernelDensityEst... | java |
public double outresScore(final int s, long[] subspace, DBIDRef id, KernelDensityEstimator kernel, DBIDs cands) {
double score = 1.0; // Initial score is 1.0
final SubspaceEuclideanDistanceFunction df = new SubspaceEuclideanDistanceFunction(subspace);
MeanVariance meanv = new MeanVariance();
ModifiableD... | java |
private DoubleDBIDList initialRange(DBIDRef obj, DBIDs cands, PrimitiveDistanceFunction<? super NumberVector> df, double eps, KernelDensityEstimator kernel, ModifiableDoubleDBIDList n) {
n.clear();
NumberVector o = kernel.relation.get(obj);
final double twoeps = eps * 2;
int matches = 0;
for(DBIDIte... | java |
private DoubleDBIDList subsetNeighborhoodQuery(DoubleDBIDList neighc, DBIDRef dbid, PrimitiveDistanceFunction<? super NumberVector> df, double adjustedEps, KernelDensityEstimator kernel, ModifiableDoubleDBIDList n) {
n.clear();
NumberVector query = kernel.relation.get(dbid);
for(DoubleDBIDListIter neighbor ... | java |
protected boolean relevantSubspace(long[] subspace, DoubleDBIDList neigh, KernelDensityEstimator kernel) {
final double crit = K_S_CRITICAL001 / FastMath.sqrt(neigh.size() - 2);
double[] data = new double[neigh.size()];
Relation<? extends NumberVector> relation = kernel.relation;
for(int dim = BitsUtil... | java |
public static double of(double... data) {
double sum = 0.;
for(double v : data) {
sum += v;
}
return sum / data.length;
} | java |
@Reference(authors = "P. M. Neely", //
title = "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients", //
booktitle = "Communications of the ACM 9(7), 1966", //
url = "https://doi.org/10.1145/365719.365958", //
bibkey = "doi:10.1145/365719.3... | java |
public void insertAll(List<E> entries) {
if(!initialized && !entries.isEmpty()) {
initialize(entries.get(0));
}
for(E entry : entries) {
insert(entry, false);
}
} | java |
protected final List<DoubleIntPair> getSortedEntries(N node, DBID q) {
List<DoubleIntPair> result = new ArrayList<>();
for(int i = 0; i < node.getNumEntries(); i++) {
E entry = node.getEntry(i);
double distance = distance(entry.getRoutingObjectID(), q);
double radius = entry.getCoveringRadius... | java |
public final double distance(E e1, E e2) {
return distance(e1.getRoutingObjectID(), e2.getRoutingObjectID());
} | java |
public static <A> double[] alphaPWM(A data, NumberArrayAdapter<?, A> adapter, final int nmom) {
final int n = adapter.size(data);
final double[] xmom = new double[nmom];
double weight = 1. / n;
for(int i = 0; i < n; i++) {
final double val = adapter.getDouble(data, i);
xmom[0] += weight * va... | java |
public static <A> double[] alphaBetaPWM(A data, NumberArrayAdapter<?, A> adapter, final int nmom) {
final int n = adapter.size(data);
final double[] xmom = new double[nmom << 1];
double aweight = 1. / n, bweight = aweight;
for(int i = 0; i < n; i++) {
final double val = adapter.getDouble(data, i);... | java |
public static <A> double[] samLMR(A sorted, NumberArrayAdapter<?, A> adapter, int nmom) {
final int n = adapter.size(sorted);
final double[] sum = new double[nmom];
nmom = n < nmom ? n : nmom;
// Estimate probability weighted moments (unbiased)
for(int i = 0; i < n; i++) {
double term = adapte... | java |
private static void normalizeLMR(double[] sum, int nmom) {
for(int k = nmom - 1; k >= 1; --k) {
double p = ((k & 1) == 0) ? +1 : -1;
double temp = p * sum[0];
for(int i = 0; i < k; i++) {
double ai = i + 1.;
p *= -(k + ai) * (k - i) / (ai * ai);
temp += p * sum[i + 1];
... | java |
private int[] countItemSupport(final Relation<BitVector> relation, final int dim) {
final int[] counts = new int[dim];
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Finding frequent 1-items", relation.size(), LOG) : null;
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advan... | java |
private FPTree buildFPTree(final Relation<BitVector> relation, int[] iidx, final int items) {
FPTree tree = new FPTree(items);
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Building FP-tree", relation.size(), LOG) : null;
int[] buf = new int[items];
for(DBIDIter iditer = relation.iterDBIDs... | java |
public StringBuilder appendTo(StringBuilder buf, VectorFieldTypeInformation<BitVector> meta) {
this.antecedent.appendTo(buf, meta);
buf.append(" --> ");
this.consequent.appendItemsTo(buf, meta);
buf.append(": ");
buf.append(union.getSupport());
buf.append(" : ");
buf.append(this.measure);
... | java |
public void process(Clustering<?> result1, Clustering<?> result2) {
// Get the clusters
final List<? extends Cluster<?>> cs1 = result1.getAllClusters();
final List<? extends Cluster<?>> cs2 = result2.getAllClusters();
// Initialize
size1 = cs1.size();
size2 = cs2.size();
contingency = new i... | java |
private long[] randomSubspace(final int alldim, final int mindim, final int maxdim, final Random rand) {
long[] dimset = BitsUtil.zero(alldim);
// Fill with all dimensions
int[] dims = new int[alldim];
for(int d = 0; d < alldim; d++) {
dims[d] = d;
}
// Target dimensionality:
int subdi... | java |
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