idx int64 0 165k | question stringlengths 73 4.15k | target stringlengths 5 918 | len_question int64 21 890 | len_target int64 3 255 |
|---|---|---|---|---|
157,400 | 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 = cove... | Determines and returns the covering radius of this node . | 113 | 12 |
157,401 | 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 ) ; } | Squared euclidean distance . 2d . | 74 | 11 |
157,402 | 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 ) ; } | Aggregate for a special value . | 57 | 7 |
157,403 | 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 ( ) ) . ... | Remove the previous relation . | 141 | 5 |
157,404 | 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 . getKnnDistan... | Determines and returns the knn distance of this node as the maximum knn distance of all entries . | 120 | 22 |
157,405 | 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 s... | Runs the VOV algorithm on the given database . | 452 | 11 |
157,406 | 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 ... | Compute variance of volumes . | 393 | 6 |
157,407 | private void boundSize ( HashSetModifiableDBIDs set , int items ) { if ( set . size ( ) > items ) { DBIDs sample = DBIDUtil . randomSample ( set , items , rnd ) ; set . clear ( ) ; set . addDBIDs ( sample ) ; } } | Bound the size of a set by random sampling . | 65 | 10 |
157,408 | 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 ) ; } | Add cand to cur s heap neighbors with distance | 69 | 9 |
157,409 | 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 ( idit... | samples newNeighbors for every object | 263 | 8 |
157,410 | private void reverse ( WritableDataStore < HashSetModifiableDBIDs > sampleNewHash , WritableDataStore < HashSetModifiableDBIDs > newReverseNeighbors , WritableDataStore < HashSetModifiableDBIDs > oldReverseNeighbors ) { for ( DBIDIter iditer = relation . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { KNN... | calculates new and old neighbors for database | 196 | 9 |
157,411 | 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 ... | Compute Jaccard similarity for two number vectors . | 228 | 11 |
157,412 | @ 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 ( ... | Performs a batch k - nearest neighbor query for a list of query objects . | 126 | 16 |
157,413 | 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... | Write a single output line . | 162 | 6 |
157,414 | 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 = ( in... | Iterate over the k range . | 272 | 7 |
157,415 | public double [ ] getCoefficients ( ) { double [ ] result = new double [ b . length ] ; System . arraycopy ( b , 0 , result , 0 , b . length ) ; return result ; } | Returns a copy of the the array of coefficients b0 ... bp . | 47 | 15 |
157,416 | 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 ; } | Returns the function value of the polynomial approximation at the specified k . | 72 | 15 |
157,417 | @ 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 , ? su... | Choose the best adapter for this . | 97 | 7 |
157,418 | 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... | OPTICS algorithm for processing a point but with different density estimates | 364 | 13 |
157,419 | 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 . resizeFile (... | Resize the matrix to cover newsize x newsize . | 122 | 12 |
157,420 | private int computeOffset ( int x , int y ) { if ( y > x ) { return computeOffset ( y , x ) ; } return ( ( x * ( x + 1 ) ) >> 1 ) + y ; } | Compute the offset within the file . | 47 | 8 |
157,421 | 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 ) ; } // Val... | Validates the header and throws an IOException if the header is invalid . If validateRecordSize is set to true the record size must match exactly the stored record size within the files header else the record size is read from the header and used . | 360 | 49 |
157,422 | 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 ( indexToFileposi... | Resize file to the intended size | 98 | 7 |
157,423 | 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 ) ; } | Read the extra header data . | 78 | 6 |
157,424 | 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 ... | Run the algorithm on a database . | 372 | 7 |
157,425 | 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 ) ... | Initializes the inter - cluster distance matrix of possible merges | 172 | 12 |
157,426 | 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 mi... | Find the best merge . | 282 | 5 |
157,427 | 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 ( ... | Merges two clusters given by x y their points with smallest IDs and y to keep | 338 | 17 |
157,428 | 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. // Upda... | Update the entries of the matrices that contain a distance to c the newly merged cluster . | 309 | 18 |
157,429 | 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... | Update entry at x y for distance matrix distances | 369 | 9 |
157,430 | 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 >... | Find the maximum distance of one object to a set . | 117 | 11 |
157,431 | @ Override public void writeExternal ( ObjectOutput out ) throws IOException { out . writeInt ( DBIDUtil . asInteger ( id ) ) ; out . writeInt ( values . length ) ; for ( double v : values ) { out . writeDouble ( v ) ; } } | Calls the super method and writes the values of this entry to the specified stream . | 61 | 17 |
157,432 | @ 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 ( ) ; } } | Calls the super method and reads the values of this entry from the specified input stream . | 82 | 18 |
157,433 | @ Override public StringBuilder appendToBuffer ( StringBuilder buf ) { buf . append ( getTask ( ) ) ; buf . append ( ": " ) ; buf . append ( getProcessed ( ) ) ; return buf ; } | Serialize indefinite progress . | 49 | 5 |
157,434 | 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 ) ; } | The input type we use . | 83 | 6 |
157,435 | 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 ; } | Test if a point is a local density maximum . | 75 | 10 |
157,436 | 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 ... | Set - based expand cluster implementation . | 322 | 7 |
157,437 | 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 .... | Collect all densities into an array for sorting . | 152 | 10 |
157,438 | 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... | Performs the CLIQUE algorithm on the given database . | 681 | 12 |
157,439 | 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... | Determines the clusters in the specified dense subspaces . | 153 | 13 |
157,440 | private List < CLIQUESubspace > findOneDimensionalDenseSubspaces ( Relation < ? extends NumberVector > database ) { List < CLIQUESubspace > denseSubspaceCandidates = findOneDimensionalDenseSubspaceCandidates ( database ) ; return prune ? pruneDenseSubspaces ( denseSubspaceCandidates ) : denseSubspaceCandidates ; } | Determines the one dimensional dense subspaces and performs a pruning if this option is chosen . | 84 | 21 |
157,441 | 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. max... | Updates the minima and maxima array according to the specified feature vector . | 134 | 16 |
157,442 | 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 ( )... | Determines the one - dimensional dense subspace candidates by making a pass over the database . | 513 | 19 |
157,443 | 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 = Do... | Performs a MDL - based pruning of the specified dense subspaces as described in the CLIQUE algorithm . | 239 | 25 |
157,444 | 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 ) . getCove... | The specified sorted list of dense subspaces is divided into the selected set I and the pruned set P . For each set the mean of the cover fractions is computed . | 209 | 35 |
157,445 | 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 ++ ) { ... | The specified sorted list of dense subspaces is divided into the selected set I and the pruned set P . For each set the difference from the specified mean values is computed . | 257 | 36 |
157,446 | public void append ( SimpleTypeInformation < ? > meta , Object data ) { this . meta . add ( meta ) ; this . contents . add ( data ) ; } | Append a single representation to the object . | 35 | 9 |
157,447 | 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 ; } | Returns whether this BitVector contains all bits that are set to true in the specified BitSet . | 80 | 19 |
157,448 | public double jaccardSimilarity ( BitVector v2 ) { return BitsUtil . intersectionSize ( bits , v2 . bits ) / ( double ) BitsUtil . unionSize ( bits , v2 . bits ) ; } | Compute the Jaccard similarity of two bit vectors . | 49 | 12 |
157,449 | 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 ; } | Write a short to the byte array at the given offset . | 58 | 12 |
157,450 | 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 ; } | Write an integer to the byte array at the given offset . | 89 | 12 |
157,451 | 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... | Write a long to the byte array at the given offset . | 154 | 12 |
157,452 | public static int writeFloat ( byte [ ] array , int offset , float v ) { return writeInt ( array , offset , Float . floatToIntBits ( v ) ) ; } | Write a float to the byte array at the given offset . | 39 | 12 |
157,453 | public static int writeDouble ( byte [ ] array , int offset , double v ) { return writeLong ( array , offset , Double . doubleToLongBits ( v ) ) ; } | Write a double to the byte array at the given offset . | 39 | 12 |
157,454 | 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 ) ) ; } | Read a short from the byte array at the given offset . | 78 | 12 |
157,455 | 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 ) ) ; } | Read an unsigned short from the byte array at the given offset . | 77 | 13 |
157,456 | 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 ) + ( b... | Read an integer from the byte array at the given offset . | 119 | 12 |
157,457 | 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 ; int... | Read a long from the byte array at the given offset . | 203 | 12 |
157,458 | 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 ) ) ; } | Write an unsigned integer using a variable - length encoding . | 95 | 11 |
157,459 | 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 ) ) ; } | Write an unsigned long using a variable - length encoding . | 96 | 11 |
157,460 | 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 ) ; } | Write a string to the buffer . | 66 | 7 |
157,461 | 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 IOException ( "Variable length quantity... | Read an unsigned integer . | 105 | 5 |
157,462 | 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 ) { // This is an ugly hack, but... | Unmap a byte buffer . | 305 | 6 |
157,463 | 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... | Depth - sort the axes . | 128 | 6 |
157,464 | 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 , i... | Sort the edges for rendering . | 132 | 6 |
157,465 | @ Override public void finalizeFirstPassE ( ) { double s = 1. / wsum ; for ( int i = 0 ; i < mean . length ; i ++ ) { mean [ i ] *= s ; } } | Finish computation of the mean . | 49 | 6 |
157,466 | private double restore ( int d , double val ) { d = ( mean . length == 1 ) ? 0 : d ; return val * mean [ d ] ; } | Restore a single dimension . | 34 | 6 |
157,467 | 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 KernelDens... | Main loop for OUTRES | 326 | 5 |
157,468 | 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 ( ) ; Modifiable... | Main loop of OUTRES . Run for each object | 481 | 10 |
157,469 | 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 (... | Initial range query . | 180 | 4 |
157,470 | 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 ( DoubleDBIDList... | Refine neighbors within a subset . | 160 | 7 |
157,471 | 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 ( ... | Subspace relevance test . | 329 | 5 |
157,472 | public static double of ( double ... data ) { double sum = 0. ; for ( double v : data ) { sum += v ; } return sum / data . length ; } | Static helper function . | 37 | 4 |
157,473 | @ 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:10.1145/365719.365958" ) public s... | Static helper function with extra precision | 192 | 6 |
157,474 | public void insertAll ( List < E > entries ) { if ( ! initialized && ! entries . isEmpty ( ) ) { initialize ( entries . get ( 0 ) ) ; } for ( E entry : entries ) { insert ( entry , false ) ; } } | Bulk insert . | 54 | 4 |
157,475 | 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 . get... | Sorts the entries of the specified node according to their minimum distance to the specified object . | 151 | 18 |
157,476 | public final double distance ( E e1 , E e2 ) { return distance ( e1 . getRoutingObjectID ( ) , e2 . getRoutingObjectID ( ) ) ; } | Returns the distance between the routing object of two entries . | 41 | 11 |
157,477 | 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 ] ... | Compute the alpha_r factors using the method of probability - weighted moments . | 157 | 16 |
157,478 | 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 . getDou... | Compute the alpha_r and beta_r factors in parallel using the method of probability - weighted moments . Usually cheaper than computing them separately . | 224 | 29 |
157,479 | 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 = ... | Compute the sample L - Moments using probability weighted moments . | 367 | 12 |
157,480 | 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 ] ; } sum [ ... | Normalize the moments | 135 | 4 |
157,481 | 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 ( ) ; idit... | Count the support of each 1 - item . | 203 | 9 |
157,482 | 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 ... | Build the actual FP - tree structure . | 292 | 8 |
157,483 | 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 to a string buffer . | 101 | 7 |
157,484 | 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 ( ) ; contin... | Process two clustering results . | 546 | 6 |
157,485 | 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 subdim ... | Choose a random subspace . | 195 | 6 |
157,486 | public Element renderCheckBox ( SVGPlot svgp , double x , double y , double size ) { // create check final Element checkmark = SVGEffects . makeCheckmark ( svgp ) ; checkmark . setAttribute ( SVGConstants . SVG_TRANSFORM_ATTRIBUTE , "scale(" + ( size / 12 ) + ") translate(" + x + " " + y + ")" ) ; if ( ! checked ) { ch... | Render the SVG checkbox to a plot | 544 | 8 |
157,487 | protected void fireSwitchEvent ( ChangeEvent evt ) { Object [ ] listeners = listenerList . getListenerList ( ) ; for ( int i = 1 ; i < listeners . length ; i += 2 ) { if ( listeners [ i - 1 ] == ChangeListener . class ) { ( ( ChangeListener ) listeners [ i ] ) . stateChanged ( evt ) ; } } } | Fire the event to listeners | 80 | 5 |
157,488 | protected static void calculateSelectivityCoeffs ( List < DoubleObjPair < DAFile > > daFiles , NumberVector query , double epsilon ) { final int dimensions = query . getDimensionality ( ) ; double [ ] lowerVals = new double [ dimensions ] ; double [ ] upperVals = new double [ dimensions ] ; VectorApproximation queryApp... | Calculate selectivity coefficients . | 343 | 7 |
157,489 | protected static VectorApproximation calculatePartialApproximation ( DBID id , NumberVector dv , List < DoubleObjPair < DAFile > > daFiles ) { int [ ] approximation = new int [ dv . getDimensionality ( ) ] ; for ( int i = 0 ; i < daFiles . size ( ) ; i ++ ) { double val = dv . doubleValue ( i ) ; double [ ] borders = d... | Calculate partial vector approximation . | 262 | 7 |
157,490 | public String solutionToString ( int fractionDigits ) { if ( ! isSolvable ( ) ) { throw new IllegalStateException ( "System is not solvable!" ) ; } DecimalFormat nf = new DecimalFormat ( ) ; nf . setMinimumFractionDigits ( fractionDigits ) ; nf . setMaximumFractionDigits ( fractionDigits ) ; nf . setDecimalFormatSymbol... | Returns a string representation of the solution of this equation system . | 378 | 12 |
157,491 | private void reducedRowEchelonForm ( int method ) { final int rows = coeff . length ; final int cols = coeff [ 0 ] . length ; int k = - 1 ; // denotes current position on diagonal int pivotRow ; // row index of pivot element int pivotCol ; // column index of pivot element double pivot ; // value of pivot element // mai... | Brings this linear equation system into reduced row echelon form with choice of pivot method . | 575 | 19 |
157,492 | private IntIntPair nonZeroPivotSearch ( int k ) { int i , j ; double absValue ; for ( i = k ; i < coeff . length ; i ++ ) { for ( j = k ; j < coeff [ 0 ] . length ; j ++ ) { // compute absolute value of // current entry in absValue absValue = Math . abs ( coeff [ row [ i ] ] [ col [ j ] ] ) ; // check if absValue is no... | Method for trivial pivot search searches for non - zero entry . | 156 | 12 |
157,493 | private void permutePivot ( IntIntPair pos1 , IntIntPair pos2 ) { int r1 = pos1 . first ; int c1 = pos1 . second ; int r2 = pos2 . first ; int c2 = pos2 . second ; int index ; index = row [ r2 ] ; row [ r2 ] = row [ r1 ] ; row [ r1 ] = index ; index = col [ c2 ] ; col [ c2 ] = col [ c1 ] ; col [ c1 ] = index ; } | permutes two matrix rows and two matrix columns | 118 | 9 |
157,494 | private void pivotOperation ( int k ) { double pivot = coeff [ row [ k ] ] [ col [ k ] ] ; // pivot row: set pivot to 1 coeff [ row [ k ] ] [ col [ k ] ] = 1 ; for ( int i = k + 1 ; i < coeff [ k ] . length ; i ++ ) { coeff [ row [ k ] ] [ col [ i ] ] /= pivot ; } rhs [ row [ k ] ] /= pivot ; if ( LOG . isDebugging ( )... | performs a pivot operation | 459 | 5 |
157,495 | private void solve ( int method ) throws NullPointerException { // solution exists if ( solved ) { return ; } // bring in reduced row echelon form if ( ! reducedRowEchelonForm ) { reducedRowEchelonForm ( method ) ; } if ( ! isSolvable ( method ) ) { if ( LOG . isDebugging ( ) ) { LOG . debugFine ( "Equation system is n... | solves linear system with the chosen method | 666 | 8 |
157,496 | private boolean isSolvable ( int method ) throws NullPointerException { if ( solved ) { return solvable ; } if ( ! reducedRowEchelonForm ) { reducedRowEchelonForm ( method ) ; } // test if rank(coeff) == rank(coeff|rhs) for ( int i = rank ; i < rhs . length ; i ++ ) { if ( Math . abs ( rhs [ row [ i ] ] ) > DELTA ) { s... | Checks solvability of this linear equation system with the chosen method . | 125 | 15 |
157,497 | private int [ ] maxIntegerDigits ( double [ ] [ ] values ) { int [ ] digits = new int [ values [ 0 ] . length ] ; for ( int j = 0 ; j < values [ 0 ] . length ; j ++ ) { for ( double [ ] value : values ) { digits [ j ] = Math . max ( digits [ j ] , integerDigits ( value [ j ] ) ) ; } } return digits ; } | Returns the maximum integer digits in each column of the specified values . | 94 | 13 |
157,498 | private int maxIntegerDigits ( double [ ] values ) { int digits = 0 ; for ( double value : values ) { digits = Math . max ( digits , integerDigits ( value ) ) ; } return digits ; } | Returns the maximum integer digits of the specified values . | 47 | 10 |
157,499 | private int integerDigits ( double d ) { double value = Math . abs ( d ) ; if ( value < 10 ) { return 1 ; } return ( int ) FastMath . log10 ( value ) + 1 ; } | Returns the integer digits of the specified double value . | 47 | 10 |
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