idx int64 0 41.2k | question stringlengths 83 4.15k | target stringlengths 5 715 |
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33,600 | private void removeRec ( O obj ) { graph . remove ( obj ) ; for ( int i = 0 ; i < numelems ; ++ i ) { if ( obj == elems [ i ] ) { System . arraycopy ( elems , i + 1 , elems , i , -- numelems - i ) ; elems [ numelems ] = null ; return ; } } } | Remove a record . |
33,601 | protected JTree createTree ( ) { JTree tree = new JTree ( model ) ; tree . setName ( "TreePopup.tree" ) ; tree . setFont ( getFont ( ) ) ; tree . setForeground ( getForeground ( ) ) ; tree . setBackground ( getBackground ( ) ) ; tree . setBorder ( null ) ; tree . setFocusable ( true ) ; tree . addMouseListener ( handler ) ; tree . addKeyListener ( handler ) ; tree . setCellRenderer ( new Renderer ( ) ) ; return tree ; } | Creates the JList used in the popup to display the items in the combo box model . This method is called when the UI class is created . |
33,602 | protected JScrollPane createScroller ( ) { JScrollPane sp = new JScrollPane ( tree , ScrollPaneConstants . VERTICAL_SCROLLBAR_AS_NEEDED , ScrollPaneConstants . HORIZONTAL_SCROLLBAR_NEVER ) ; sp . setHorizontalScrollBar ( null ) ; sp . setName ( "TreePopup.scrollPane" ) ; sp . setFocusable ( false ) ; sp . getVerticalScrollBar ( ) . setFocusable ( false ) ; sp . setBorder ( null ) ; return sp ; } | Creates the scroll pane which houses the scrollable tree . |
33,603 | public void show ( Component parent ) { Dimension parentSize = parent . getSize ( ) ; Insets insets = getInsets ( ) ; parentSize . setSize ( parentSize . width - ( insets . right + insets . left ) , 10 * parentSize . height ) ; Dimension scrollSize = computePopupBounds ( parent , 0 , getBounds ( ) . height , parentSize . width , parentSize . height ) . getSize ( ) ; scroller . setMaximumSize ( scrollSize ) ; scroller . setPreferredSize ( scrollSize ) ; scroller . setMinimumSize ( scrollSize ) ; super . show ( parent , 0 , parent . getHeight ( ) ) ; tree . requestFocusInWindow ( ) ; } | Display the popup attached to the given component . |
33,604 | protected void fireActionPerformed ( ActionEvent event ) { Object [ ] listeners = listenerList . getListenerList ( ) ; for ( int i = listeners . length - 2 ; i >= 0 ; i -= 2 ) { if ( listeners [ i ] == ActionListener . class ) { ( ( ActionListener ) listeners [ i + 1 ] ) . actionPerformed ( event ) ; } } } | Notify action listeners . |
33,605 | private < T > void setCached ( String prefix , String postfix , T data ) { cache . put ( prefix + '.' + postfix , data ) ; } | Set a cache value |
33,606 | protected String getPropertyValue ( String prefix , String postfix ) { String ret = properties . getProperty ( prefix + "." + postfix ) ; if ( ret != null ) { return ret ; } int pos = prefix . length ( ) ; while ( pos > 0 ) { pos = prefix . lastIndexOf ( '.' , pos - 1 ) ; if ( pos <= 0 ) { break ; } ret = properties . getProperty ( prefix . substring ( 0 , pos ) + '.' + postfix ) ; if ( ret != null ) { return ret ; } } ret = properties . getProperty ( postfix ) ; if ( ret != null ) { return ret ; } return null ; } | Retrieve the property value for a particular path + type pair . |
33,607 | @ SuppressWarnings ( "unchecked" ) public static < V extends NumberVector > NumberVector . Factory < V > guessFactory ( SimpleTypeInformation < V > in ) { NumberVector . Factory < V > factory = null ; if ( in instanceof VectorTypeInformation ) { factory = ( NumberVector . Factory < V > ) ( ( VectorTypeInformation < V > ) in ) . getFactory ( ) ; } if ( factory == null ) { try { Field f = in . getRestrictionClass ( ) . getField ( "FACTORY" ) ; factory = ( NumberVector . Factory < V > ) f . get ( null ) ; } catch ( Exception e ) { LoggingUtil . warning ( "Cannot determine factory for type " + in . getRestrictionClass ( ) , e ) ; } } return factory ; } | Try to guess the appropriate factory . |
33,608 | public void appendSimple ( Object ... data ) { if ( data . length != meta . size ( ) ) { throw new AbortException ( "Invalid number of attributes in 'append'." ) ; } for ( int i = 0 ; i < data . length ; i ++ ) { @ SuppressWarnings ( "unchecked" ) final List < Object > col = ( List < Object > ) columns . get ( i ) ; col . add ( data [ i ] ) ; } } | Append a new record to the data set . Pay attention to having the right number of values! |
33,609 | public static MultipleObjectsBundle fromStream ( BundleStreamSource source ) { MultipleObjectsBundle bundle = new MultipleObjectsBundle ( ) ; boolean stop = false ; DBIDVar var = null ; ArrayModifiableDBIDs ids = null ; int size = 0 ; while ( ! stop ) { BundleStreamSource . Event ev = source . nextEvent ( ) ; switch ( ev ) { case END_OF_STREAM : stop = true ; break ; case META_CHANGED : BundleMeta smeta = source . getMeta ( ) ; bundle . meta = new BundleMeta ( ) ; for ( int i = 0 ; i < bundle . columns . size ( ) ; i ++ ) { bundle . meta . add ( smeta . get ( i ) ) ; } for ( int i = bundle . metaLength ( ) ; i < smeta . size ( ) ; i ++ ) { List < Object > data = new ArrayList < > ( bundle . dataLength ( ) + 1 ) ; bundle . appendColumn ( smeta . get ( i ) , data ) ; } if ( var == null && source . hasDBIDs ( ) ) { var = DBIDUtil . newVar ( ) ; ids = DBIDUtil . newArray ( ) ; } continue ; case NEXT_OBJECT : if ( var != null && source . assignDBID ( var ) ) { ids . add ( var ) ; } for ( int i = 0 ; i < bundle . metaLength ( ) ; i ++ ) { @ SuppressWarnings ( "unchecked" ) final List < Object > col = ( List < Object > ) bundle . columns . get ( i ) ; col . add ( source . data ( i ) ) ; } ++ size ; continue ; default : LoggingUtil . warning ( "Unknown event: " + ev ) ; continue ; } } if ( ids != null ) { if ( size != ids . size ( ) ) { LOG . warning ( "Not every object had an DBID - discarding DBIDs: " + size + " != " + ids . size ( ) ) ; } else { bundle . setDBIDs ( ids ) ; } } return bundle ; } | Convert an object stream to a bundle |
33,610 | public Object [ ] getRow ( int row ) { Object [ ] ret = new Object [ columns . size ( ) ] ; for ( int c = 0 ; c < columns . size ( ) ; c ++ ) { ret [ c ] = data ( row , c ) ; } return ret ; } | Get an object row . |
33,611 | protected void batchNN ( AbstractRStarTreeNode < ? , ? > node , Map < DBID , KNNHeap > knnLists ) { if ( node . isLeaf ( ) ) { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { SpatialEntry p = node . getEntry ( i ) ; for ( Entry < DBID , KNNHeap > ent : knnLists . entrySet ( ) ) { final DBID q = ent . getKey ( ) ; final KNNHeap knns_q = ent . getValue ( ) ; double knn_q_maxDist = knns_q . getKNNDistance ( ) ; DBID pid = ( ( LeafEntry ) p ) . getDBID ( ) ; double dist_pq = distanceFunction . distance ( relation . get ( pid ) , relation . get ( q ) ) ; tree . statistics . countDistanceCalculation ( ) ; if ( dist_pq <= knn_q_maxDist ) { knns_q . insert ( dist_pq , pid ) ; } } } } else { ModifiableDBIDs ids = DBIDUtil . newArray ( knnLists . size ( ) ) ; for ( DBID id : knnLists . keySet ( ) ) { ids . add ( id ) ; } List < DoubleDistanceEntry > entries = getSortedEntries ( node , ids ) ; for ( DoubleDistanceEntry distEntry : entries ) { double minDist = distEntry . distance ; for ( Entry < DBID , KNNHeap > ent : knnLists . entrySet ( ) ) { final KNNHeap knns_q = ent . getValue ( ) ; double knn_q_maxDist = knns_q . getKNNDistance ( ) ; if ( minDist <= knn_q_maxDist ) { SpatialEntry entry = distEntry . entry ; AbstractRStarTreeNode < ? , ? > child = tree . getNode ( ( ( DirectoryEntry ) entry ) . getPageID ( ) ) ; batchNN ( child , knnLists ) ; break ; } } } } } | Performs a batch knn query . |
33,612 | protected List < DoubleDistanceEntry > getSortedEntries ( AbstractRStarTreeNode < ? , ? > node , DBIDs ids ) { List < DoubleDistanceEntry > result = new ArrayList < > ( ) ; for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { SpatialEntry entry = node . getEntry ( i ) ; double minMinDist = Double . MAX_VALUE ; for ( DBIDIter iter = ids . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { double minDist = distanceFunction . minDist ( entry , relation . get ( iter ) ) ; tree . statistics . countDistanceCalculation ( ) ; minMinDist = Math . min ( minDist , minMinDist ) ; } result . add ( new DoubleDistanceEntry ( entry , minMinDist ) ) ; } Collections . sort ( result ) ; return result ; } | Sorts the entries of the specified node according to their minimum distance to the specified objects . |
33,613 | private boolean checkCandidateUpdate ( double [ ] point ) { final double x = point [ 0 ] , y = point [ 1 ] ; if ( points . isEmpty ( ) ) { leftx = rightx = x ; topy = bottomy = y ; topleft = topright = bottomleft = bottomright = point ; return true ; } if ( x <= leftx || x >= rightx || y <= bottomy || y >= topy ) { double xpy = x + y , xmy = x - y ; boolean changed = false ; if ( xpy < bottomleft [ 0 ] + bottomleft [ 1 ] ) { bottomleft = point ; changed = true ; } else if ( xpy > topright [ 0 ] + topright [ 1 ] ) { topright = point ; changed = true ; } if ( xmy < topleft [ 0 ] - topleft [ 1 ] ) { topleft = point ; changed = true ; } else if ( xmy > bottomright [ 0 ] - bottomright [ 1 ] ) { bottomright = point ; changed = true ; } if ( changed ) { leftx = Math . max ( bottomleft [ 0 ] , topleft [ 0 ] ) ; rightx = Math . min ( bottomright [ 0 ] , topright [ 0 ] ) ; topy = Math . min ( topleft [ 1 ] , topright [ 1 ] ) ; bottomy = Math . max ( bottomleft [ 1 ] , bottomright [ 1 ] ) ; } return true ; } return false ; } | Check whether a point is inside the current bounds and update the bounds |
33,614 | static DBIDs randomSample ( DBIDs ids , int samplesize , Random rnd , DBIDs previous ) { if ( previous == null ) { return DBIDUtil . randomSample ( ids , samplesize , rnd ) ; } ModifiableDBIDs sample = DBIDUtil . newHashSet ( samplesize ) ; sample . addDBIDs ( previous ) ; sample . addDBIDs ( DBIDUtil . randomSample ( ids , samplesize - previous . size ( ) , rnd ) ) ; if ( sample . size ( ) < samplesize ) { for ( DBIDIter it = DBIDUtil . randomSample ( ids , samplesize , rnd ) . iter ( ) ; sample . size ( ) < samplesize && it . valid ( ) ; it . advance ( ) ) { sample . add ( it ) ; } } return sample ; } | Draw a random sample of the desired size . |
33,615 | public void actionPerformed ( ActionEvent e ) { final JFileChooser fc = new JFileChooser ( new File ( "." ) ) ; if ( param . isDefined ( ) ) { fc . setSelectedFile ( param . getValue ( ) ) ; } if ( e . getSource ( ) == button ) { int returnVal = fc . showOpenDialog ( button ) ; if ( returnVal == JFileChooser . APPROVE_OPTION ) { textfield . setText ( fc . getSelectedFile ( ) . getPath ( ) ) ; fireValueChanged ( ) ; } } else if ( e . getSource ( ) == textfield ) { fireValueChanged ( ) ; } else { LoggingUtil . warning ( "actionPerformed triggered by unknown source: " + e . getSource ( ) ) ; } } | Button callback to show the file selector |
33,616 | protected Node inlineThumbnail ( Document doc , ParsedURL urldata , Node eold ) { RenderableImage img = ThumbnailRegistryEntry . handleURL ( urldata ) ; if ( img == null ) { LoggingUtil . warning ( "Image not found in registry: " + urldata . toString ( ) ) ; return null ; } ByteArrayOutputStream os = new ByteArrayOutputStream ( ) ; try { os . write ( SVGSyntax . DATA_PROTOCOL_PNG_PREFIX . getBytes ( ) ) ; Base64EncoderStream encoder = new Base64EncoderStream ( os ) ; ImageIO . write ( img . createDefaultRendering ( ) , "png" , encoder ) ; encoder . close ( ) ; } catch ( IOException e ) { LoggingUtil . exception ( "Exception serializing image to png" , e ) ; return null ; } Element i = ( Element ) super . cloneNode ( doc , eold ) ; i . setAttributeNS ( SVGConstants . XLINK_NAMESPACE_URI , SVGConstants . XLINK_HREF_ATTRIBUTE , os . toString ( ) . replaceAll ( "\\s*[\\r\\n]+\\s*" , "" ) ) ; return i ; } | Inline a referenced thumbnail . |
33,617 | private static PrintStream openStream ( File out ) throws IOException { OutputStream os = new FileOutputStream ( out ) ; os = out . getName ( ) . endsWith ( GZIP_POSTFIX ) ? new GZIPOutputStream ( os ) : os ; return new PrintStream ( os ) ; } | Open the output stream using gzip if necessary . |
33,618 | public void setDimensionality ( int dimensionality ) throws IllegalArgumentException { final int maxdim = getMaxDim ( ) ; if ( maxdim > dimensionality ) { throw new IllegalArgumentException ( "Given dimensionality " + dimensionality + " is too small w.r.t. the given values (occurring maximum: " + maxdim + ")." ) ; } this . dimensionality = dimensionality ; } | Sets the dimensionality to the new value . |
33,619 | protected IndexTreePath < E > findPathToObject ( IndexTreePath < E > subtree , SpatialComparable mbr , DBIDRef id ) { N node = getNode ( subtree . getEntry ( ) ) ; if ( node . isLeaf ( ) ) { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { if ( DBIDUtil . equal ( ( ( LeafEntry ) node . getEntry ( i ) ) . getDBID ( ) , id ) ) { return new IndexTreePath < > ( subtree , node . getEntry ( i ) , i ) ; } } } else { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { if ( SpatialUtil . intersects ( node . getEntry ( i ) , mbr ) ) { IndexTreePath < E > childSubtree = new IndexTreePath < > ( subtree , node . getEntry ( i ) , i ) ; IndexTreePath < E > path = findPathToObject ( childSubtree , mbr , id ) ; if ( path != null ) { return path ; } } } } return null ; } | Returns the path to the leaf entry in the specified subtree that represents the data object with the specified mbr and id . |
33,620 | protected void deletePath ( IndexTreePath < E > deletionPath ) { N leaf = getNode ( deletionPath . getParentPath ( ) . getEntry ( ) ) ; int index = deletionPath . getIndex ( ) ; E entry = leaf . getEntry ( index ) ; leaf . deleteEntry ( index ) ; writeNode ( leaf ) ; Stack < N > stack = new Stack < > ( ) ; condenseTree ( deletionPath . getParentPath ( ) , stack ) ; while ( ! stack . empty ( ) ) { N node = stack . pop ( ) ; if ( node . isLeaf ( ) ) { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { settings . getOverflowTreatment ( ) . reinitialize ( ) ; this . insertLeafEntry ( node . getEntry ( i ) ) ; } } else { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { stack . push ( getNode ( node . getEntry ( i ) ) ) ; } } deleteNode ( node ) ; } postDelete ( entry ) ; doExtraIntegrityChecks ( ) ; } | Delete a leaf at a given path - deletions for non - leaves are not supported! |
33,621 | protected List < E > createBulkLeafNodes ( List < E > objects ) { int minEntries = leafMinimum ; int maxEntries = leafCapacity ; ArrayList < E > result = new ArrayList < > ( ) ; List < List < E > > partitions = settings . bulkSplitter . partition ( objects , minEntries , maxEntries ) ; for ( List < E > partition : partitions ) { N leafNode = createNewLeafNode ( ) ; for ( E o : partition ) { leafNode . addLeafEntry ( o ) ; } writeNode ( leafNode ) ; result . add ( createNewDirectoryEntry ( leafNode ) ) ; if ( getLogger ( ) . isDebugging ( ) ) { getLogger ( ) . debugFine ( "Created leaf page " + leafNode . getPageID ( ) ) ; } } if ( getLogger ( ) . isDebugging ( ) ) { getLogger ( ) . debugFine ( "numDataPages = " + result . size ( ) ) ; } return result ; } | Creates and returns the leaf nodes for bulk load . |
33,622 | protected IndexTreePath < E > choosePath ( IndexTreePath < E > subtree , SpatialComparable mbr , int depth , int cur ) { if ( getLogger ( ) . isDebuggingFiner ( ) ) { getLogger ( ) . debugFiner ( "node " + subtree + ", depth " + depth ) ; } N node = getNode ( subtree . getEntry ( ) ) ; if ( node == null ) { throw new RuntimeException ( "Page file did not return node for node id: " + getPageID ( subtree . getEntry ( ) ) ) ; } if ( node . isLeaf ( ) ) { return subtree ; } IndexTreePath < E > newSubtree = containedTest ( subtree , node , mbr ) ; if ( newSubtree != null ) { return ( ++ cur == depth ) ? newSubtree : choosePath ( newSubtree , mbr , depth , cur ) ; } N childNode = getNode ( node . getEntry ( 0 ) ) ; int num = settings . insertionStrategy . choose ( node , NodeArrayAdapter . STATIC , mbr , height , cur ) ; newSubtree = new IndexTreePath < > ( subtree , node . getEntry ( num ) , num ) ; ++ cur ; if ( cur == depth ) { return newSubtree ; } if ( childNode . isLeaf ( ) ) { assert cur == newSubtree . getPathCount ( ) ; throw new IllegalArgumentException ( "childNode is leaf, but currentDepth != depth: " + cur + " != " + depth ) ; } return choosePath ( newSubtree , mbr , depth , cur ) ; } | Chooses the best path of the specified subtree for insertion of the given mbr at the specified level . |
33,623 | private N split ( N node ) { int minimum = node . isLeaf ( ) ? leafMinimum : dirMinimum ; long [ ] split = settings . nodeSplitter . split ( node , NodeArrayAdapter . STATIC , minimum ) ; final N newNode = node . isLeaf ( ) ? createNewLeafNode ( ) : createNewDirectoryNode ( ) ; node . splitByMask ( newNode , split ) ; writeNode ( node ) ; writeNode ( newNode ) ; return newNode ; } | Splits the specified node and returns the newly created split node . |
33,624 | public void reInsert ( N node , IndexTreePath < E > path , int [ ] offs ) { final int depth = path . getPathCount ( ) ; long [ ] remove = BitsUtil . zero ( node . getCapacity ( ) ) ; List < E > reInsertEntries = new ArrayList < > ( offs . length ) ; for ( int i = 0 ; i < offs . length ; i ++ ) { reInsertEntries . add ( node . getEntry ( offs [ i ] ) ) ; BitsUtil . setI ( remove , offs [ i ] ) ; } node . removeMask ( remove ) ; writeNode ( node ) ; IndexTreePath < E > childPath = path ; N child = node ; while ( childPath . getParentPath ( ) != null ) { N parent = getNode ( childPath . getParentPath ( ) . getEntry ( ) ) ; int indexOfChild = childPath . getIndex ( ) ; if ( child . adjustEntry ( parent . getEntry ( indexOfChild ) ) ) { writeNode ( parent ) ; childPath = childPath . getParentPath ( ) ; child = parent ; } else { break ; } } final Logging log = getLogger ( ) ; for ( E entry : reInsertEntries ) { if ( node . isLeaf ( ) ) { if ( log . isDebugging ( ) ) { log . debug ( "reinsert " + entry ) ; } insertLeafEntry ( entry ) ; } else { if ( log . isDebugging ( ) ) { log . debug ( "reinsert " + entry + " at " + depth ) ; } insertDirectoryEntry ( entry , depth ) ; } } } | Reinserts the specified node at the specified level . |
33,625 | private void condenseTree ( IndexTreePath < E > subtree , Stack < N > stack ) { N node = getNode ( subtree . getEntry ( ) ) ; if ( ! isRoot ( node ) ) { N parent = getNode ( subtree . getParentPath ( ) . getEntry ( ) ) ; int index = subtree . getIndex ( ) ; if ( hasUnderflow ( node ) ) { if ( parent . deleteEntry ( index ) ) { stack . push ( node ) ; } else { node . adjustEntry ( parent . getEntry ( index ) ) ; } } else { node . adjustEntry ( parent . getEntry ( index ) ) ; } writeNode ( parent ) ; condenseTree ( subtree . getParentPath ( ) , stack ) ; } else { if ( hasUnderflow ( node ) && node . getNumEntries ( ) == 1 && ! node . isLeaf ( ) ) { N child = getNode ( node . getEntry ( 0 ) ) ; final N newRoot ; if ( child . isLeaf ( ) ) { newRoot = createNewLeafNode ( ) ; newRoot . setPageID ( getRootID ( ) ) ; for ( int i = 0 ; i < child . getNumEntries ( ) ; i ++ ) { newRoot . addLeafEntry ( child . getEntry ( i ) ) ; } } else { newRoot = createNewDirectoryNode ( ) ; newRoot . setPageID ( getRootID ( ) ) ; for ( int i = 0 ; i < child . getNumEntries ( ) ; i ++ ) { newRoot . addDirectoryEntry ( child . getEntry ( i ) ) ; } } writeNode ( newRoot ) ; height -- ; } } } | Condenses the tree after deletion of some nodes . |
33,626 | private void getLeafNodes ( N node , List < E > result , int currentLevel ) { if ( currentLevel == 2 ) { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { result . add ( node . getEntry ( i ) ) ; } } else { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { getLeafNodes ( getNode ( node . getEntry ( i ) ) , result , ( currentLevel - 1 ) ) ; } } } | Determines the entries pointing to the leaf nodes of the specified subtree . |
33,627 | public static double angleDense ( NumberVector v1 , NumberVector v2 ) { final int dim1 = v1 . getDimensionality ( ) , dim2 = v2 . getDimensionality ( ) ; final int mindim = ( dim1 <= dim2 ) ? dim1 : dim2 ; double cross = 0 , l1 = 0 , l2 = 0 ; for ( int k = 0 ; k < mindim ; k ++ ) { final double r1 = v1 . doubleValue ( k ) ; final double r2 = v2 . doubleValue ( k ) ; cross += r1 * r2 ; l1 += r1 * r1 ; l2 += r2 * r2 ; } for ( int k = mindim ; k < dim1 ; k ++ ) { final double r1 = v1 . doubleValue ( k ) ; l1 += r1 * r1 ; } for ( int k = mindim ; k < dim2 ; k ++ ) { final double r2 = v2 . doubleValue ( k ) ; l2 += r2 * r2 ; } final double a = ( cross == 0. ) ? 0. : ( l1 == 0. || l2 == 0. ) ? 1. : FastMath . sqrt ( ( cross / l1 ) * ( cross / l2 ) ) ; return ( a < 1. ) ? a : 1. ; } | Compute the absolute cosine of the angle between two dense vectors . |
33,628 | public static double angleSparse ( SparseNumberVector v1 , SparseNumberVector v2 ) { double l1 = 0. , l2 = 0. , cross = 0. ; int i1 = v1 . iter ( ) , i2 = v2 . iter ( ) ; while ( v1 . iterValid ( i1 ) && v2 . iterValid ( i2 ) ) { final int d1 = v1 . iterDim ( i1 ) , d2 = v2 . iterDim ( i2 ) ; if ( d1 < d2 ) { final double val = v1 . iterDoubleValue ( i1 ) ; l1 += val * val ; i1 = v1 . iterAdvance ( i1 ) ; } else if ( d2 < d1 ) { final double val = v2 . iterDoubleValue ( i2 ) ; l2 += val * val ; i2 = v2 . iterAdvance ( i2 ) ; } else { final double val1 = v1 . iterDoubleValue ( i1 ) ; final double val2 = v2 . iterDoubleValue ( i2 ) ; l1 += val1 * val1 ; l2 += val2 * val2 ; cross += val1 * val2 ; i1 = v1 . iterAdvance ( i1 ) ; i2 = v2 . iterAdvance ( i2 ) ; } } while ( v1 . iterValid ( i1 ) ) { final double val = v1 . iterDoubleValue ( i1 ) ; l1 += val * val ; i1 = v1 . iterAdvance ( i1 ) ; } while ( v2 . iterValid ( i2 ) ) { final double val = v2 . iterDoubleValue ( i2 ) ; l2 += val * val ; i2 = v2 . iterAdvance ( i2 ) ; } final double a = ( cross == 0. ) ? 0. : ( l1 == 0. || l2 == 0. ) ? 1. : FastMath . sqrt ( ( cross / l1 ) * ( cross / l2 ) ) ; return ( a < 1. ) ? a : 1. ; } | Compute the angle for sparse vectors . |
33,629 | public static double angleSparseDense ( SparseNumberVector v1 , NumberVector v2 ) { final int dim2 = v2 . getDimensionality ( ) ; double l1 = 0. , l2 = 0. , cross = 0. ; int i1 = v1 . iter ( ) , d2 = 0 ; while ( v1 . iterValid ( i1 ) ) { final int d1 = v1 . iterDim ( i1 ) ; while ( d2 < d1 && d2 < dim2 ) { final double val = v2 . doubleValue ( d2 ) ; l2 += val * val ; ++ d2 ; } if ( d2 < dim2 ) { assert ( d1 == d2 ) : "Dimensions not ordered" ; final double val1 = v1 . iterDoubleValue ( i1 ) ; final double val2 = v2 . doubleValue ( d2 ) ; l1 += val1 * val1 ; l2 += val2 * val2 ; cross += val1 * val2 ; i1 = v1 . iterAdvance ( i1 ) ; ++ d2 ; } else { final double val = v1 . iterDoubleValue ( i1 ) ; l1 += val * val ; i1 = v1 . iterAdvance ( i1 ) ; } } while ( d2 < dim2 ) { final double val = v2 . doubleValue ( d2 ) ; l2 += val * val ; ++ d2 ; } final double a = ( cross == 0. ) ? 0. : ( l1 == 0. || l2 == 0. ) ? 1. : FastMath . sqrt ( ( cross / l1 ) * ( cross / l2 ) ) ; return ( a < 1. ) ? a : 1. ; } | Compute the angle for a sparse and a dense vector . |
33,630 | public static double cosAngle ( NumberVector v1 , NumberVector v2 ) { return v1 instanceof SparseNumberVector ? v2 instanceof SparseNumberVector ? angleSparse ( ( SparseNumberVector ) v1 , ( SparseNumberVector ) v2 ) : angleSparseDense ( ( SparseNumberVector ) v1 , v2 ) : v2 instanceof SparseNumberVector ? angleSparseDense ( ( SparseNumberVector ) v2 , v1 ) : angleDense ( v1 , v2 ) ; } | Compute the absolute cosine of the angle between two vectors . |
33,631 | public static double minCosAngle ( SpatialComparable v1 , SpatialComparable v2 ) { if ( v1 instanceof NumberVector && v2 instanceof NumberVector ) { return cosAngle ( ( NumberVector ) v1 , ( NumberVector ) v2 ) ; } final int dim1 = v1 . getDimensionality ( ) , dim2 = v2 . getDimensionality ( ) ; final int mindim = ( dim1 <= dim2 ) ? dim1 : dim2 ; double s1 = 0 , s2 = 0 , l1 = 0 , l2 = 0 ; for ( int k = 0 ; k < mindim ; k ++ ) { final double min1 = v1 . getMin ( k ) , max1 = v1 . getMax ( k ) ; final double min2 = v2 . getMin ( k ) , max2 = v2 . getMax ( k ) ; final double p1 = min1 * min2 , p2 = min1 * max2 ; final double p3 = max1 * min2 , p4 = max1 * max2 ; s1 += Math . max ( Math . max ( p1 , p2 ) , Math . max ( p3 , p4 ) ) ; s2 += Math . min ( Math . min ( p1 , p2 ) , Math . min ( p3 , p4 ) ) ; if ( max1 < 0 ) { l1 += max1 * max1 ; } else if ( min1 > 0 ) { l1 += min1 * min1 ; } if ( max2 < 0 ) { l2 += max2 * max2 ; } else if ( min2 > 0 ) { l2 += min2 * min2 ; } } for ( int k = mindim ; k < dim1 ; k ++ ) { final double min1 = v1 . getMin ( k ) , max1 = v1 . getMax ( k ) ; if ( max1 < 0. ) { l1 += max1 * max1 ; } else if ( min1 > 0. ) { l1 += min1 * min1 ; } } for ( int k = mindim ; k < dim2 ; k ++ ) { final double min2 = v2 . getMin ( k ) , max2 = v2 . getMax ( k ) ; if ( max2 < 0. ) { l2 += max2 * max2 ; } else if ( min2 > 0. ) { l2 += min2 * min2 ; } } final double cross = Math . max ( Math . abs ( s1 ) , Math . abs ( s2 ) ) ; final double a = ( cross == 0. ) ? 0. : ( l1 == 0. || l2 == 0. ) ? 1. : FastMath . sqrt ( ( cross / l1 ) * ( cross / l2 ) ) ; return ( a < 1. ) ? a : 1. ; } | Compute the minimum angle between two rectangles . |
33,632 | public static double angle ( NumberVector v1 , NumberVector v2 , NumberVector o ) { final int dim1 = v1 . getDimensionality ( ) , dim2 = v2 . getDimensionality ( ) , dimo = o . getDimensionality ( ) ; final int mindim = ( dim1 <= dim2 ) ? dim1 : dim2 ; double cross = 0 , l1 = 0 , l2 = 0 ; for ( int k = 0 ; k < mindim ; k ++ ) { final double ok = k < dimo ? o . doubleValue ( k ) : 0. ; final double r1 = v1 . doubleValue ( k ) - ok ; final double r2 = v2 . doubleValue ( k ) - ok ; cross += r1 * r2 ; l1 += r1 * r1 ; l2 += r2 * r2 ; } for ( int k = mindim ; k < dim1 ; k ++ ) { final double ok = k < dimo ? o . doubleValue ( k ) : 0. ; final double r1 = v1 . doubleValue ( k ) - ok ; l1 += r1 * r1 ; } for ( int k = mindim ; k < dim2 ; k ++ ) { final double ok = k < dimo ? o . doubleValue ( k ) : 0. ; final double r2 = v2 . doubleValue ( k ) - ok ; l2 += r2 * r2 ; } final double a = ( cross == 0. ) ? 0. : ( l1 == 0. || l2 == 0. ) ? 1. : FastMath . sqrt ( ( cross / l1 ) * ( cross / l2 ) ) ; return ( a < 1. ) ? a : 1. ; } | Compute the angle between two vectors with respect to a reference point . |
33,633 | public static double dotDense ( NumberVector v1 , NumberVector v2 ) { final int dim1 = v1 . getDimensionality ( ) , dim2 = v2 . getDimensionality ( ) ; final int mindim = ( dim1 <= dim2 ) ? dim1 : dim2 ; double dot = 0 ; for ( int k = 0 ; k < mindim ; k ++ ) { dot += v1 . doubleValue ( k ) * v2 . doubleValue ( k ) ; } return dot ; } | Compute the dot product of two dense vectors . |
33,634 | public static double dotSparse ( SparseNumberVector v1 , SparseNumberVector v2 ) { double dot = 0. ; int i1 = v1 . iter ( ) , i2 = v2 . iter ( ) ; while ( v1 . iterValid ( i1 ) && v2 . iterValid ( i2 ) ) { final int d1 = v1 . iterDim ( i1 ) , d2 = v2 . iterDim ( i2 ) ; if ( d1 < d2 ) { i1 = v1 . iterAdvance ( i1 ) ; } else if ( d2 < d1 ) { i2 = v2 . iterAdvance ( i2 ) ; } else { dot += v1 . iterDoubleValue ( i1 ) * v2 . iterDoubleValue ( i2 ) ; i1 = v1 . iterAdvance ( i1 ) ; i2 = v2 . iterAdvance ( i2 ) ; } } return dot ; } | Compute the dot product for two sparse vectors . |
33,635 | public static double dotSparseDense ( SparseNumberVector v1 , NumberVector v2 ) { final int dim2 = v2 . getDimensionality ( ) ; double dot = 0. ; for ( int i1 = v1 . iter ( ) ; v1 . iterValid ( i1 ) ; ) { final int d1 = v1 . iterDim ( i1 ) ; if ( d1 >= dim2 ) { break ; } dot += v1 . iterDoubleValue ( i1 ) * v2 . doubleValue ( d1 ) ; i1 = v1 . iterAdvance ( i1 ) ; } return dot ; } | Compute the dot product for a sparse and a dense vector . |
33,636 | public static double dot ( NumberVector v1 , NumberVector v2 ) { return v1 instanceof SparseNumberVector ? v2 instanceof SparseNumberVector ? dotSparse ( ( SparseNumberVector ) v1 , ( SparseNumberVector ) v2 ) : dotSparseDense ( ( SparseNumberVector ) v1 , v2 ) : v2 instanceof SparseNumberVector ? dotSparseDense ( ( SparseNumberVector ) v2 , v1 ) : dotDense ( v1 , v2 ) ; } | Compute the dot product of the angle between two vectors . |
33,637 | public static double minDot ( SpatialComparable v1 , SpatialComparable v2 ) { if ( v1 instanceof NumberVector && v2 instanceof NumberVector ) { return dot ( ( NumberVector ) v1 , ( NumberVector ) v2 ) ; } final int dim1 = v1 . getDimensionality ( ) , dim2 = v2 . getDimensionality ( ) ; final int mindim = ( dim1 <= dim2 ) ? dim1 : dim2 ; double s1 = 0 , s2 = 0 ; for ( int k = 0 ; k < mindim ; k ++ ) { final double min1 = v1 . getMin ( k ) , max1 = v1 . getMax ( k ) ; final double min2 = v2 . getMin ( k ) , max2 = v2 . getMax ( k ) ; final double p1 = min1 * min2 , p2 = min1 * max2 ; final double p3 = max1 * min2 , p4 = max1 * max2 ; s1 += Math . max ( Math . max ( p1 , p2 ) , Math . max ( p3 , p4 ) ) ; s2 += Math . min ( Math . min ( p1 , p2 ) , Math . min ( p3 , p4 ) ) ; } return Math . max ( Math . abs ( s1 ) , Math . abs ( s2 ) ) ; } | Compute the minimum angle between two rectangles assuming unit length vectors |
33,638 | public static < V extends NumberVector > V project ( V v , long [ ] selectedAttributes , NumberVector . Factory < V > factory ) { int card = BitsUtil . cardinality ( selectedAttributes ) ; if ( factory instanceof SparseNumberVector . Factory ) { final SparseNumberVector . Factory < ? > sfactory = ( SparseNumberVector . Factory < ? > ) factory ; Int2DoubleOpenHashMap values = new Int2DoubleOpenHashMap ( card , .8f ) ; for ( int d = BitsUtil . nextSetBit ( selectedAttributes , 0 ) ; d >= 0 ; d = BitsUtil . nextSetBit ( selectedAttributes , d + 1 ) ) { if ( v . doubleValue ( d ) != 0.0 ) { values . put ( d , v . doubleValue ( d ) ) ; } } @ SuppressWarnings ( "unchecked" ) V projectedVector = ( V ) sfactory . newNumberVector ( values , card ) ; return projectedVector ; } else { double [ ] newAttributes = new double [ card ] ; int i = 0 ; for ( int d = BitsUtil . nextSetBit ( selectedAttributes , 0 ) ; d >= 0 ; d = BitsUtil . nextSetBit ( selectedAttributes , d + 1 ) ) { newAttributes [ i ] = v . doubleValue ( d ) ; i ++ ; } return factory . newNumberVector ( newAttributes ) ; } } | Project a number vector to the specified attributes . |
33,639 | public void mergeWith ( Core o ) { o . num = this . num = ( num < o . num ? num : o . num ) ; } | Merge two cores . |
33,640 | public Clustering < Model > run ( Relation < ? > relation ) { HashMap < String , DBIDs > labelMap = multiple ? multipleAssignment ( relation ) : singleAssignment ( relation ) ; ModifiableDBIDs noiseids = DBIDUtil . newArray ( ) ; Clustering < Model > result = new Clustering < > ( "By Label Clustering" , "bylabel-clustering" ) ; for ( Entry < String , DBIDs > entry : labelMap . entrySet ( ) ) { DBIDs ids = entry . getValue ( ) ; if ( ids . size ( ) <= 1 ) { noiseids . addDBIDs ( ids ) ; continue ; } Cluster < Model > c = new Cluster < Model > ( entry . getKey ( ) , ids , ClusterModel . CLUSTER ) ; if ( noisepattern != null && noisepattern . matcher ( entry . getKey ( ) ) . find ( ) ) { c . setNoise ( true ) ; } result . addToplevelCluster ( c ) ; } if ( noiseids . size ( ) > 0 ) { Cluster < Model > c = new Cluster < Model > ( "Noise" , noiseids , ClusterModel . CLUSTER ) ; c . setNoise ( true ) ; result . addToplevelCluster ( c ) ; } return result ; } | Run the actual clustering algorithm . |
33,641 | private HashMap < String , DBIDs > singleAssignment ( Relation < ? > data ) { HashMap < String , DBIDs > labelMap = new HashMap < > ( ) ; for ( DBIDIter iditer = data . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { final Object val = data . get ( iditer ) ; String label = ( val != null ) ? val . toString ( ) : null ; assign ( labelMap , label , iditer ) ; } return labelMap ; } | Assigns the objects of the database to single clusters according to their labels . |
33,642 | private HashMap < String , DBIDs > multipleAssignment ( Relation < ? > data ) { HashMap < String , DBIDs > labelMap = new HashMap < > ( ) ; for ( DBIDIter iditer = data . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { String [ ] labels = data . get ( iditer ) . toString ( ) . split ( " " ) ; for ( String label : labels ) { assign ( labelMap , label , iditer ) ; } } return labelMap ; } | Assigns the objects of the database to multiple clusters according to their labels . |
33,643 | private void assign ( HashMap < String , DBIDs > labelMap , String label , DBIDRef id ) { if ( labelMap . containsKey ( label ) ) { DBIDs exist = labelMap . get ( label ) ; if ( exist instanceof DBID ) { ModifiableDBIDs n = DBIDUtil . newHashSet ( ) ; n . add ( ( DBID ) exist ) ; n . add ( id ) ; labelMap . put ( label , n ) ; } else { assert ( exist instanceof HashSetModifiableDBIDs ) ; assert ( exist . size ( ) > 1 ) ; ( ( ModifiableDBIDs ) exist ) . add ( id ) ; } } else { labelMap . put ( label , DBIDUtil . deref ( id ) ) ; } } | Assigns the specified id to the labelMap according to its label |
33,644 | public void put ( double val ) { min = val < min ? val : min ; max = val > max ? val : max ; } | Process a single double value . |
33,645 | public static void addShadowFilter ( SVGPlot svgp ) { Element shadow = svgp . getIdElement ( SHADOW_ID ) ; if ( shadow == null ) { shadow = svgp . svgElement ( SVGConstants . SVG_FILTER_TAG ) ; shadow . setAttribute ( SVGConstants . SVG_ID_ATTRIBUTE , SHADOW_ID ) ; shadow . setAttribute ( SVGConstants . SVG_WIDTH_ATTRIBUTE , "140%" ) ; shadow . setAttribute ( SVGConstants . SVG_HEIGHT_ATTRIBUTE , "140%" ) ; Element offset = svgp . svgElement ( SVGConstants . SVG_FE_OFFSET_TAG ) ; offset . setAttribute ( SVGConstants . SVG_IN_ATTRIBUTE , SVGConstants . SVG_SOURCE_ALPHA_VALUE ) ; offset . setAttribute ( SVGConstants . SVG_RESULT_ATTRIBUTE , "off" ) ; offset . setAttribute ( SVGConstants . SVG_DX_ATTRIBUTE , "0.1" ) ; offset . setAttribute ( SVGConstants . SVG_DY_ATTRIBUTE , "0.1" ) ; shadow . appendChild ( offset ) ; Element gauss = svgp . svgElement ( SVGConstants . SVG_FE_GAUSSIAN_BLUR_TAG ) ; gauss . setAttribute ( SVGConstants . SVG_IN_ATTRIBUTE , "off" ) ; gauss . setAttribute ( SVGConstants . SVG_RESULT_ATTRIBUTE , "blur" ) ; gauss . setAttribute ( SVGConstants . SVG_STD_DEVIATION_ATTRIBUTE , "0.1" ) ; shadow . appendChild ( gauss ) ; Element blend = svgp . svgElement ( SVGConstants . SVG_FE_BLEND_TAG ) ; blend . setAttribute ( SVGConstants . SVG_IN_ATTRIBUTE , SVGConstants . SVG_SOURCE_GRAPHIC_VALUE ) ; blend . setAttribute ( SVGConstants . SVG_IN2_ATTRIBUTE , "blur" ) ; blend . setAttribute ( SVGConstants . SVG_MODE_ATTRIBUTE , SVGConstants . SVG_NORMAL_VALUE ) ; shadow . appendChild ( blend ) ; svgp . getDefs ( ) . appendChild ( shadow ) ; svgp . putIdElement ( SHADOW_ID , shadow ) ; } } | Static method to prepare a SVG document for drop shadow effects . |
33,646 | public static void addLightGradient ( SVGPlot svgp ) { Element gradient = svgp . getIdElement ( LIGHT_GRADIENT_ID ) ; if ( gradient == null ) { gradient = svgp . svgElement ( SVGConstants . SVG_LINEAR_GRADIENT_TAG ) ; gradient . setAttribute ( SVGConstants . SVG_ID_ATTRIBUTE , LIGHT_GRADIENT_ID ) ; gradient . setAttribute ( SVGConstants . SVG_X1_ATTRIBUTE , "0" ) ; gradient . setAttribute ( SVGConstants . SVG_Y1_ATTRIBUTE , "0" ) ; gradient . setAttribute ( SVGConstants . SVG_X2_ATTRIBUTE , "0" ) ; gradient . setAttribute ( SVGConstants . SVG_Y2_ATTRIBUTE , "1" ) ; Element stop0 = svgp . svgElement ( SVGConstants . SVG_STOP_TAG ) ; stop0 . setAttribute ( SVGConstants . SVG_STOP_COLOR_ATTRIBUTE , "white" ) ; stop0 . setAttribute ( SVGConstants . SVG_STOP_OPACITY_ATTRIBUTE , "1" ) ; stop0 . setAttribute ( SVGConstants . SVG_OFFSET_ATTRIBUTE , "0" ) ; gradient . appendChild ( stop0 ) ; Element stop04 = svgp . svgElement ( SVGConstants . SVG_STOP_TAG ) ; stop04 . setAttribute ( SVGConstants . SVG_STOP_COLOR_ATTRIBUTE , "white" ) ; stop04 . setAttribute ( SVGConstants . SVG_STOP_OPACITY_ATTRIBUTE , "0" ) ; stop04 . setAttribute ( SVGConstants . SVG_OFFSET_ATTRIBUTE , ".4" ) ; gradient . appendChild ( stop04 ) ; Element stop06 = svgp . svgElement ( SVGConstants . SVG_STOP_TAG ) ; stop06 . setAttribute ( SVGConstants . SVG_STOP_COLOR_ATTRIBUTE , "black" ) ; stop06 . setAttribute ( SVGConstants . SVG_STOP_OPACITY_ATTRIBUTE , "0" ) ; stop06 . setAttribute ( SVGConstants . SVG_OFFSET_ATTRIBUTE , ".6" ) ; gradient . appendChild ( stop06 ) ; Element stop1 = svgp . svgElement ( SVGConstants . SVG_STOP_TAG ) ; stop1 . setAttribute ( SVGConstants . SVG_STOP_COLOR_ATTRIBUTE , "black" ) ; stop1 . setAttribute ( SVGConstants . SVG_STOP_OPACITY_ATTRIBUTE , ".5" ) ; stop1 . setAttribute ( SVGConstants . SVG_OFFSET_ATTRIBUTE , "1" ) ; gradient . appendChild ( stop1 ) ; svgp . getDefs ( ) . appendChild ( gradient ) ; svgp . putIdElement ( LIGHT_GRADIENT_ID , gradient ) ; } } | Static method to prepare a SVG document for light gradient effects . |
33,647 | public static Element makeCheckmark ( SVGPlot svgp ) { Element checkmark = svgp . svgElement ( SVGConstants . SVG_PATH_TAG ) ; checkmark . setAttribute ( SVGConstants . SVG_D_ATTRIBUTE , SVG_CHECKMARK_PATH ) ; checkmark . setAttribute ( SVGConstants . SVG_FILL_ATTRIBUTE , SVGConstants . CSS_BLACK_VALUE ) ; checkmark . setAttribute ( SVGConstants . SVG_STROKE_ATTRIBUTE , SVGConstants . CSS_NONE_VALUE ) ; return checkmark ; } | Creates a 15x15 big checkmark |
33,648 | public double continueToMargin ( double [ ] origin , double [ ] delta ) { assert ( delta . length == 2 && origin . length == 2 ) ; double factor = Double . POSITIVE_INFINITY ; if ( delta [ 0 ] > 0 ) { factor = Math . min ( factor , ( maxx - origin [ 0 ] ) / delta [ 0 ] ) ; } else if ( delta [ 0 ] < 0 ) { factor = Math . min ( factor , ( origin [ 0 ] - minx ) / - delta [ 0 ] ) ; } if ( delta [ 1 ] > 0 ) { factor = Math . min ( factor , ( maxy - origin [ 1 ] ) / delta [ 1 ] ) ; } else if ( delta [ 1 ] < 0 ) { factor = Math . min ( factor , ( origin [ 1 ] - miny ) / - delta [ 1 ] ) ; } return factor ; } | Continue a line along a given direction to the margin . |
33,649 | public void clear ( ) { try { file . setLength ( header . size ( ) ) ; } catch ( IOException e ) { throw new RuntimeException ( e ) ; } } | Clears this PageFile . |
33,650 | private double deviation ( double [ ] delta , double [ ] [ ] beta ) { final double a = squareSum ( delta ) ; final double b = squareSum ( transposeTimes ( beta , delta ) ) ; return ( a > b ) ? FastMath . sqrt ( a - b ) : 0. ; } | Deviation from a manifold described by beta . |
33,651 | private Separation findSeparation ( Relation < NumberVector > relation , DBIDs currentids , int dimension , Random r ) { Separation separation = new Separation ( ) ; int samples = ( int ) Math . min ( LOG_NOT_FROM_ONE_CLUSTER_PROBABILITY / ( FastMath . log1p ( - FastMath . powFast ( samplingLevel , - dimension ) ) ) , ( double ) currentids . size ( ) ) ; int remaining_retries = 100 ; for ( int i = 1 ; i <= samples ; i ++ ) { DBIDs sample = DBIDUtil . randomSample ( currentids , dimension + 1 , r ) ; final DBIDIter iter = sample . iter ( ) ; double [ ] originV = relation . get ( iter ) . toArray ( ) ; iter . advance ( ) ; double [ ] [ ] basis ; { List < double [ ] > vectors = new ArrayList < > ( sample . size ( ) - 1 ) ; for ( ; iter . valid ( ) ; iter . advance ( ) ) { double [ ] vec = relation . get ( iter ) . toArray ( ) ; vectors . add ( minusEquals ( vec , originV ) ) ; } basis = generateOrthonormalBasis ( vectors ) ; if ( basis == null ) { i -- ; if ( -- remaining_retries < 0 ) { throw new TooManyRetriesException ( "Too many retries in sampling, and always a linear dependant data set." ) ; } continue ; } } DoubleDynamicHistogram histogram = new DoubleDynamicHistogram ( BINS ) ; double w = 1.0 / currentids . size ( ) ; for ( DBIDIter iter2 = currentids . iter ( ) ; iter2 . valid ( ) ; iter2 . advance ( ) ) { if ( sample . contains ( iter2 ) ) { continue ; } double [ ] vec = minusEquals ( relation . get ( iter2 ) . toArray ( ) , originV ) ; final double distance = deviation ( vec , basis ) ; histogram . increment ( distance , w ) ; } double [ ] th = findAndEvaluateThreshold ( histogram ) ; if ( th [ 1 ] > separation . goodness ) { separation . goodness = th [ 1 ] ; separation . threshold = th [ 0 ] ; separation . originV = originV ; separation . basis = basis ; } } return separation ; } | This method samples a number of linear manifolds an tries to determine which the one with the best cluster is . |
33,652 | public double getDistance ( final DBIDRef o1 , final DBIDRef o2 ) { return FastMath . sqrt ( getSquaredDistance ( o1 , o2 ) ) ; } | Returns the kernel distance between the two specified objects . |
33,653 | public double getSquaredDistance ( final DBIDRef id1 , final DBIDRef id2 ) { final int o1 = idmap . getOffset ( id1 ) , o2 = idmap . getOffset ( id2 ) ; return kernel [ o1 ] [ o1 ] + kernel [ o2 ] [ o2 ] - 2 * kernel [ o1 ] [ o2 ] ; } | Returns the squared kernel distance between the two specified objects . |
33,654 | public double getSimilarity ( DBIDRef id1 , DBIDRef id2 ) { return kernel [ idmap . getOffset ( id1 ) ] [ idmap . getOffset ( id2 ) ] ; } | Get the kernel similarity for the given objects . |
33,655 | protected double [ ] [ ] initialMeans ( Database database , Relation < V > relation ) { Duration inittime = getLogger ( ) . newDuration ( initializer . getClass ( ) + ".time" ) . begin ( ) ; double [ ] [ ] means = initializer . chooseInitialMeans ( database , relation , k , getDistanceFunction ( ) ) ; getLogger ( ) . statistics ( inittime . end ( ) ) ; return means ; } | Choose the initial means . |
33,656 | public static void plusEquals ( double [ ] sum , NumberVector vec ) { for ( int d = 0 ; d < sum . length ; d ++ ) { sum [ d ] += vec . doubleValue ( d ) ; } } | Similar to VMath . plusEquals but accepts a number vector . |
33,657 | public static void minusEquals ( double [ ] sum , NumberVector vec ) { for ( int d = 0 ; d < sum . length ; d ++ ) { sum [ d ] -= vec . doubleValue ( d ) ; } } | Similar to VMath . minusEquals but accepts a number vector . |
33,658 | public static void plusMinusEquals ( double [ ] add , double [ ] sub , NumberVector vec ) { for ( int d = 0 ; d < add . length ; d ++ ) { final double v = vec . doubleValue ( d ) ; add [ d ] += v ; sub [ d ] -= v ; } } | Add to one remove from another . |
33,659 | protected static void incrementalUpdateMean ( double [ ] mean , NumberVector vec , int newsize , double op ) { if ( newsize == 0 ) { return ; } VMath . plusTimesEquals ( mean , VMath . minusEquals ( vec . toArray ( ) , mean ) , op / newsize ) ; } | Compute an incremental update for the mean . |
33,660 | public static int fastModPrime ( long data ) { int high = ( int ) ( data >>> 32 ) ; int alpha = ( ( int ) data ) + ( high << 2 + high ) ; if ( alpha < 0 && alpha > - 5 ) { alpha = alpha + 5 ; } return alpha ; } | Fast modulo operation for the largest unsigned integer prime . |
33,661 | private void doRangeQuery ( DBID o_p , AbstractMTreeNode < O , ? , ? > node , O q , double r_q , ModifiableDoubleDBIDList result ) { double d1 = 0. ; if ( o_p != null ) { d1 = distanceQuery . distance ( o_p , q ) ; index . statistics . countDistanceCalculation ( ) ; } if ( ! node . isLeaf ( ) ) { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { MTreeEntry entry = node . getEntry ( i ) ; DBID o_r = entry . getRoutingObjectID ( ) ; double r_or = entry . getCoveringRadius ( ) ; double d2 = o_p != null ? entry . getParentDistance ( ) : 0. ; double diff = Math . abs ( d1 - d2 ) ; double sum = r_q + r_or ; if ( diff <= sum ) { double d3 = distanceQuery . distance ( o_r , q ) ; index . statistics . countDistanceCalculation ( ) ; if ( d3 <= sum ) { AbstractMTreeNode < O , ? , ? > child = index . getNode ( ( ( DirectoryEntry ) entry ) . getPageID ( ) ) ; doRangeQuery ( o_r , child , q , r_q , result ) ; } } } } else { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { MTreeEntry entry = node . getEntry ( i ) ; DBID o_j = entry . getRoutingObjectID ( ) ; double d2 = o_p != null ? entry . getParentDistance ( ) : 0. ; double diff = Math . abs ( d1 - d2 ) ; if ( diff <= r_q ) { double d3 = distanceQuery . distance ( o_j , q ) ; index . statistics . countDistanceCalculation ( ) ; if ( d3 <= r_q ) { result . add ( d3 , o_j ) ; } } } } } | Performs a range query on the specified subtree . It recursively traverses all paths from the specified node which cannot be excluded from leading to qualifying objects . |
33,662 | public static double pdf ( double x , double mu , double beta ) { final double z = ( x - mu ) / beta ; if ( x == Double . NEGATIVE_INFINITY ) { return 0. ; } return FastMath . exp ( - z - FastMath . exp ( - z ) ) / beta ; } | PDF of Gumbel distribution |
33,663 | public static double logpdf ( double x , double mu , double beta ) { if ( x == Double . NEGATIVE_INFINITY ) { return Double . NEGATIVE_INFINITY ; } final double z = ( x - mu ) / beta ; return - z - FastMath . exp ( - z ) - FastMath . log ( beta ) ; } | log PDF of Gumbel distribution |
33,664 | public static double cdf ( double val , double mu , double beta ) { return FastMath . exp ( - FastMath . exp ( - ( val - mu ) / beta ) ) ; } | CDF of Gumbel distribution |
33,665 | public static double quantile ( double val , double mu , double beta ) { return mu - beta * FastMath . log ( - FastMath . log ( val ) ) ; } | Quantile function of Gumbel distribution |
33,666 | public void setPartitions ( Relation < V > relation ) throws IllegalArgumentException { if ( ( FastMath . log ( partitions ) / FastMath . log ( 2 ) ) != ( int ) ( FastMath . log ( partitions ) / FastMath . log ( 2 ) ) ) { throw new IllegalArgumentException ( "Number of partitions must be a power of 2!" ) ; } final int dimensions = RelationUtil . dimensionality ( relation ) ; final int size = relation . size ( ) ; splitPositions = new double [ dimensions ] [ partitions + 1 ] ; for ( int d = 0 ; d < dimensions ; d ++ ) { double [ ] tempdata = new double [ size ] ; int j = 0 ; for ( DBIDIter iditer = relation . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { tempdata [ j ] = relation . get ( iditer ) . doubleValue ( d ) ; j += 1 ; } Arrays . sort ( tempdata ) ; for ( int b = 0 ; b < partitions ; b ++ ) { int start = ( int ) ( b * size / ( double ) partitions ) ; splitPositions [ d ] [ b ] = tempdata [ start ] ; } splitPositions [ d ] [ partitions ] = tempdata [ size - 1 ] + 0.000001 ; } } | Initialize the data set grid by computing quantiles . |
33,667 | public long getScannedPages ( ) { int vacapacity = pageSize / VectorApproximation . byteOnDisk ( splitPositions . length , partitions ) ; long vasize = ( long ) Math . ceil ( ( vectorApprox . size ( ) ) / ( 1.0 * vacapacity ) ) ; return vasize * scans ; } | Get the number of scanned bytes . |
33,668 | private void hqr2BackTransformation ( int nn , int low , int high ) { for ( int j = nn - 1 ; j >= low ; j -- ) { final int last = j < high ? j : high ; for ( int i = low ; i <= high ; i ++ ) { final double [ ] Vi = V [ i ] ; double sum = 0. ; for ( int k = low ; k <= last ; k ++ ) { sum += Vi [ k ] * H [ k ] [ j ] ; } Vi [ j ] = sum ; } } } | Back transformation to get eigenvectors of original matrix . |
33,669 | protected static double gammaQuantileNewtonRefinement ( final double logpt , final double k , final double theta , final int maxit , double x ) { final double EPS_N = 1e-15 ; if ( x <= 0 ) { x = Double . MIN_NORMAL ; } double logpc = logcdf ( x , k , theta ) ; if ( x == Double . MIN_NORMAL && logpc > logpt * ( 1. + 1e-7 ) ) { return 0. ; } if ( logpc == Double . NEGATIVE_INFINITY ) { return 0. ; } for ( int i = 0 ; i < maxit ; i ++ ) { final double logpe = logpc - logpt ; if ( Math . abs ( logpe ) < Math . abs ( EPS_N * logpt ) ) { break ; } final double g = logpdf ( x , k , theta ) ; if ( g == Double . NEGATIVE_INFINITY ) { break ; } final double newx = x - logpe * FastMath . exp ( logpc - g ) ; logpc = logcdf ( newx , k , theta ) ; if ( Math . abs ( logpc - logpt ) > Math . abs ( logpe ) || ( i > 0 && Math . abs ( logpc - logpt ) == Math . abs ( logpe ) ) ) { break ; } x = newx ; } return x ; } | Refinement of ChiSquared probit using Newton iterations . |
33,670 | public Element useMarker ( SVGPlot plot , Element parent , double x , double y , int stylenr , double size ) { Element marker = plot . svgCircle ( x , y , size * .5 ) ; final String col ; if ( stylenr == - 1 ) { col = dotcolor ; } else if ( stylenr == - 2 ) { col = greycolor ; } else { col = colors . getColor ( stylenr ) ; } SVGUtil . setStyle ( marker , SVGConstants . CSS_FILL_PROPERTY + ":" + col ) ; parent . appendChild ( marker ) ; return marker ; } | Use a given marker on the document . |
33,671 | public Clustering < DendrogramModel > run ( PointerHierarchyRepresentationResult pointerresult ) { Clustering < DendrogramModel > result = new Instance ( pointerresult ) . run ( ) ; result . addChildResult ( pointerresult ) ; return result ; } | Process an existing result . |
33,672 | public static double erf ( double x ) { final double w = x < 0 ? - x : x ; double y ; if ( w < 2.2 ) { double t = w * w ; int k = ( int ) t ; t -= k ; k *= 13 ; y = ( ( ( ( ( ( ( ( ( ( ( ( ERF_COEFF1 [ k ] * t + ERF_COEFF1 [ k + 1 ] ) * t + ERF_COEFF1 [ k + 2 ] ) * t + ERF_COEFF1 [ k + 3 ] ) * t + ERF_COEFF1 [ k + 4 ] ) * t + ERF_COEFF1 [ k + 5 ] ) * t + ERF_COEFF1 [ k + 6 ] ) * t + ERF_COEFF1 [ k + 7 ] ) * t + ERF_COEFF1 [ k + 8 ] ) * t + ERF_COEFF1 [ k + 9 ] ) * t + ERF_COEFF1 [ k + 10 ] ) * t + ERF_COEFF1 [ k + 11 ] ) * t + ERF_COEFF1 [ k + 12 ] ) * w ; } else if ( w < 6.9 ) { int k = ( int ) w ; double t = w - k ; k = 13 * ( k - 2 ) ; y = ( ( ( ( ( ( ( ( ( ( ( ERF_COEFF2 [ k ] * t + ERF_COEFF2 [ k + 1 ] ) * t + ERF_COEFF2 [ k + 2 ] ) * t + ERF_COEFF2 [ k + 3 ] ) * t + ERF_COEFF2 [ k + 4 ] ) * t + ERF_COEFF2 [ k + 5 ] ) * t + ERF_COEFF2 [ k + 6 ] ) * t + ERF_COEFF2 [ k + 7 ] ) * t + ERF_COEFF2 [ k + 8 ] ) * t + ERF_COEFF2 [ k + 9 ] ) * t + ERF_COEFF2 [ k + 10 ] ) * t + ERF_COEFF2 [ k + 11 ] ) * t + ERF_COEFF2 [ k + 12 ] ; y *= y ; y *= y ; y *= y ; y = 1 - y * y ; } else if ( w == w ) { y = 1 ; } else { return Double . NaN ; } return x < 0 ? - y : y ; } | Error function for Gaussian distributions = Normal distributions . |
33,673 | public static double standardNormalQuantile ( double d ) { return ( d == 0 ) ? Double . NEGATIVE_INFINITY : ( d == 1 ) ? Double . POSITIVE_INFINITY : ( Double . isNaN ( d ) || d < 0 || d > 1 ) ? Double . NaN : MathUtil . SQRT2 * - erfcinv ( 2 * d ) ; } | Approximate the inverse error function for normal distributions . |
33,674 | public < N extends SpatialComparable > List < List < N > > partition ( List < N > spatialObjects , int minEntries , int maxEntries ) { List < List < N > > partitions = new ArrayList < > ( ) ; List < N > objects = new ArrayList < > ( spatialObjects ) ; while ( ! objects . isEmpty ( ) ) { StringBuilder msg = new StringBuilder ( ) ; int splitAxis = chooseMaximalExtendedSplitAxis ( objects ) ; int splitPoint = chooseBulkSplitPoint ( objects . size ( ) , minEntries , maxEntries ) ; if ( LOG . isDebugging ( ) ) { msg . append ( "\nsplitAxis " ) . append ( splitAxis ) ; msg . append ( "\nsplitPoint " ) . append ( splitPoint ) ; } Collections . sort ( objects , new SpatialSingleMinComparator ( splitAxis ) ) ; List < N > partition1 = new ArrayList < > ( ) ; for ( int i = 0 ; i < splitPoint ; i ++ ) { N o = objects . remove ( 0 ) ; partition1 . add ( o ) ; } partitions . add ( partition1 ) ; if ( LOG . isDebugging ( ) ) { msg . append ( "\ncurrent partition " ) . append ( partition1 ) ; msg . append ( "\nremaining objects # " ) . append ( objects . size ( ) ) ; LOG . debugFine ( msg . toString ( ) ) ; } } if ( LOG . isDebugging ( ) ) { LOG . debugFine ( "partitions " + partitions ) ; } return partitions ; } | Partitions the specified feature vectors where the split axes are the dimensions with maximum extension . |
33,675 | private int chooseMaximalExtendedSplitAxis ( List < ? extends SpatialComparable > objects ) { int dimension = objects . get ( 0 ) . getDimensionality ( ) ; double [ ] maxExtension = new double [ dimension ] ; double [ ] minExtension = new double [ dimension ] ; Arrays . fill ( minExtension , Double . MAX_VALUE ) ; for ( SpatialComparable object : objects ) { for ( int d = 0 ; d < dimension ; d ++ ) { double min , max ; min = object . getMin ( d ) ; max = object . getMax ( d ) ; if ( maxExtension [ d ] < max ) { maxExtension [ d ] = max ; } if ( minExtension [ d ] > min ) { minExtension [ d ] = min ; } } } int splitAxis = - 1 ; double max = 0 ; for ( int d = 0 ; d < dimension ; d ++ ) { double currentExtension = maxExtension [ d ] - minExtension [ d ] ; if ( max < currentExtension ) { max = currentExtension ; splitAxis = d ; } } return splitAxis ; } | Computes and returns the best split axis . The best split axis is the split axes with the maximal extension . |
33,676 | public void setTotal ( int total ) throws IllegalArgumentException { if ( getProcessed ( ) > total ) { throw new IllegalArgumentException ( getProcessed ( ) + " exceeds total: " + total ) ; } this . total = total ; } | Modify the total value . |
33,677 | @ SuppressWarnings ( "unchecked" ) protected < T > T get ( DBIDRef id , int index ) { Object [ ] d = data . get ( DBIDUtil . deref ( id ) ) ; if ( d == null ) { return null ; } return ( T ) d [ index ] ; } | Actual getter . |
33,678 | @ SuppressWarnings ( "unchecked" ) protected < T > T set ( DBIDRef id , int index , T value ) { Object [ ] d = data . get ( DBIDUtil . deref ( id ) ) ; if ( d == null ) { d = new Object [ rlen ] ; data . put ( DBIDUtil . deref ( id ) , d ) ; } T ret = ( T ) d [ index ] ; d [ index ] = value ; return ret ; } | Actual setter . |
33,679 | public UniformDistribution estimate ( DoubleMinMax mm ) { return new UniformDistribution ( Math . max ( mm . getMin ( ) , - Double . MAX_VALUE ) , Math . min ( mm . getMax ( ) , Double . MAX_VALUE ) ) ; } | Estimate parameters from minimum and maximum observed . |
33,680 | public static boolean canVisualize ( Relation < ? > rel , AbstractMTree < ? , ? , ? , ? > tree ) { if ( ! TypeUtil . NUMBER_VECTOR_FIELD . isAssignableFromType ( rel . getDataTypeInformation ( ) ) ) { return false ; } return getLPNormP ( tree ) > 0 ; } | Test for a visualizable index in the context s database . |
33,681 | void initializeRandomAttributes ( SimpleTypeInformation < V > in ) { int d = ( ( VectorFieldTypeInformation < V > ) in ) . getDimensionality ( ) ; selectedAttributes = BitsUtil . random ( k , d , rnd . getSingleThreadedRandom ( ) ) ; } | Initialize random attributes . |
33,682 | protected void singleEnsemble ( final double [ ] ensemble , final NumberVector vec ) { double [ ] buf = new double [ 1 ] ; for ( int i = 0 ; i < ensemble . length ; i ++ ) { buf [ 0 ] = vec . doubleValue ( i ) ; ensemble [ i ] = voting . combine ( buf , 1 ) ; if ( Double . isNaN ( ensemble [ i ] ) ) { LOG . warning ( "NaN after combining: " + FormatUtil . format ( buf ) + " " + voting . toString ( ) ) ; } } applyScaling ( ensemble , scaling ) ; } | Build a single - element ensemble . |
33,683 | public static String getFullDescription ( Parameter < ? > param ) { StringBuilder description = new StringBuilder ( 1000 ) . append ( param . getShortDescription ( ) ) . append ( FormatUtil . NEWLINE ) ; param . describeValues ( description ) ; if ( ! FormatUtil . endsWith ( description , FormatUtil . NEWLINE ) ) { description . append ( FormatUtil . NEWLINE ) ; } if ( param . hasDefaultValue ( ) ) { description . append ( "Default: " ) . append ( param . getDefaultValueAsString ( ) ) . append ( FormatUtil . NEWLINE ) ; } List < ? extends ParameterConstraint < ? > > constraints = param . getConstraints ( ) ; if ( constraints != null && ! constraints . isEmpty ( ) ) { description . append ( ( constraints . size ( ) == 1 ) ? "Constraint: " : "Constraints: " ) . append ( constraints . get ( 0 ) . getDescription ( param . getOptionID ( ) . getName ( ) ) ) ; for ( int i = 1 ; i < constraints . size ( ) ; i ++ ) { description . append ( ", " ) . append ( constraints . get ( i ) . getDescription ( param . getOptionID ( ) . getName ( ) ) ) ; } description . append ( FormatUtil . NEWLINE ) ; } return description . toString ( ) ; } | Format a parameter description . |
33,684 | private static void println ( StringBuilder buf , int width , String data ) { for ( String line : FormatUtil . splitAtLastBlank ( data , width ) ) { buf . append ( line ) ; if ( ! line . endsWith ( FormatUtil . NEWLINE ) ) { buf . append ( FormatUtil . NEWLINE ) ; } } } | Simple writing helper with no indentation . |
33,685 | public static int centroids ( Relation < ? extends NumberVector > rel , List < ? extends Cluster < ? > > clusters , NumberVector [ ] centroids , NoiseHandling noiseOption ) { assert ( centroids . length == clusters . size ( ) ) ; int ignorednoise = 0 ; Iterator < ? extends Cluster < ? > > ci = clusters . iterator ( ) ; for ( int i = 0 ; ci . hasNext ( ) ; i ++ ) { Cluster < ? > cluster = ci . next ( ) ; if ( cluster . size ( ) <= 1 || cluster . isNoise ( ) ) { switch ( noiseOption ) { case IGNORE_NOISE : ignorednoise += cluster . size ( ) ; case TREAT_NOISE_AS_SINGLETONS : centroids [ i ] = null ; continue ; case MERGE_NOISE : break ; } } centroids [ i ] = ModelUtil . getPrototypeOrCentroid ( cluster . getModel ( ) , rel , cluster . getIDs ( ) ) ; } return ignorednoise ; } | Compute centroids . |
33,686 | public static double cdf ( double val , double rate ) { final double v = .5 * FastMath . exp ( - rate * Math . abs ( val ) ) ; return ( v == Double . POSITIVE_INFINITY ) ? ( ( val <= 0 ) ? 0 : 1 ) : ( val < 0 ) ? v : 1 - v ; } | Cumulative density static version |
33,687 | protected double maxDistance ( DoubleDBIDList elems ) { double max = 0 ; for ( DoubleDBIDListIter it = elems . iter ( ) ; it . valid ( ) ; it . advance ( ) ) { final double v = it . doubleValue ( ) ; max = max > v ? max : v ; } return max ; } | Find maximum in a list via scanning . |
33,688 | protected void excludeNotCovered ( ModifiableDoubleDBIDList candidates , double fmax , ModifiableDoubleDBIDList collect ) { for ( DoubleDBIDListIter it = candidates . iter ( ) ; it . valid ( ) ; ) { if ( it . doubleValue ( ) > fmax ) { collect . add ( it . doubleValue ( ) , it ) ; candidates . removeSwap ( it . getOffset ( ) ) ; } else { it . advance ( ) ; } } } | Retain all elements within the current cover . |
33,689 | protected void collectByCover ( DBIDRef cur , ModifiableDoubleDBIDList candidates , double fmax , ModifiableDoubleDBIDList collect ) { assert ( collect . size ( ) == 0 ) : "Not empty" ; DoubleDBIDListIter it = candidates . iter ( ) . advance ( ) ; while ( it . valid ( ) ) { assert ( ! DBIDUtil . equal ( cur , it ) ) ; final double dist = distance ( cur , it ) ; if ( dist <= fmax ) { collect . add ( dist , it ) ; candidates . removeSwap ( it . getOffset ( ) ) ; } else { it . advance ( ) ; } } } | Collect all elements with respect to a new routing object . |
33,690 | private void process ( double [ ] data , double min , double max , KernelDensityFunction kernel , int window , double epsilon ) { dens = new double [ data . length ] ; var = new double [ data . length ] ; double halfwidth = ( ( max - min ) / window ) * .5 ; for ( int current = 0 ; current < data . length ; current ++ ) { double value = 0.0 ; for ( int i = current ; i >= 0 ; i -- ) { double delta = Math . abs ( data [ i ] - data [ current ] ) / halfwidth ; final double contrib = kernel . density ( delta ) ; value += contrib ; if ( contrib < epsilon ) { break ; } } for ( int i = current + 1 ; i < data . length ; i ++ ) { double delta = Math . abs ( data [ i ] - data [ current ] ) / halfwidth ; final double contrib = kernel . density ( delta ) ; value += contrib ; if ( contrib < epsilon ) { break ; } } double realwidth = ( Math . min ( data [ current ] + halfwidth , max ) - Math . max ( min , data [ current ] - halfwidth ) ) ; double weight = realwidth / ( 2 * halfwidth ) ; dens [ current ] = value / ( data . length * realwidth * .5 ) ; var [ current ] = 1 / weight ; } } | Process a new array |
33,691 | public static double [ ] computeSimilarityMatrix ( DependenceMeasure sim , Relation < ? extends NumberVector > rel ) { final int dim = RelationUtil . dimensionality ( rel ) ; double [ ] [ ] data = new double [ dim ] [ rel . size ( ) ] ; int r = 0 ; for ( DBIDIter it = rel . iterDBIDs ( ) ; it . valid ( ) ; it . advance ( ) , r ++ ) { NumberVector v = rel . get ( it ) ; for ( int d = 0 ; d < dim ; d ++ ) { data [ d ] [ r ] = v . doubleValue ( d ) ; } } return sim . dependence ( DoubleArrayAdapter . STATIC , Arrays . asList ( data ) ) ; } | Compute a column - wise dependency matrix for the given relation . |
33,692 | protected N buildSpanningTree ( int dim , double [ ] mat , Layout layout ) { assert ( layout . edges == null || layout . edges . size ( ) == 0 ) ; int [ ] iedges = PrimsMinimumSpanningTree . processDense ( mat , new LowerTriangularAdapter ( dim ) ) ; int root = findOptimalRoot ( iedges ) ; ArrayList < Edge > edges = new ArrayList < > ( iedges . length >> 1 ) ; for ( int i = 1 ; i < iedges . length ; i += 2 ) { edges . add ( new Edge ( iedges [ i - 1 ] , iedges [ i ] ) ) ; } layout . edges = edges ; ArrayList < N > nodes = new ArrayList < > ( dim ) ; for ( int i = 0 ; i < dim ; i ++ ) { nodes . add ( null ) ; } layout . nodes = nodes ; N rootnode = buildTree ( iedges , root , - 1 , nodes ) ; return rootnode ; } | Build the minimum spanning tree . |
33,693 | protected N buildTree ( int [ ] msg , int cur , int parent , ArrayList < N > nodes ) { int c = 0 ; for ( int i = 1 ; i < msg . length ; i += 2 ) { if ( ( msg [ i - 1 ] == cur && msg [ i ] != parent ) || ( msg [ i ] == cur && msg [ i - 1 ] != parent ) ) { c ++ ; } } List < N > children = Collections . emptyList ( ) ; if ( c > 0 ) { children = new ArrayList < > ( c ) ; for ( int i = 1 ; i < msg . length ; i += 2 ) { if ( msg [ i - 1 ] == cur && msg [ i ] != parent ) { children . add ( buildTree ( msg , msg [ i ] , cur , nodes ) ) ; } else if ( msg [ i ] == cur && msg [ i - 1 ] != parent ) { children . add ( buildTree ( msg , msg [ i - 1 ] , cur , nodes ) ) ; } } } N node = makeNode ( cur , children ) ; nodes . set ( cur , node ) ; return node ; } | Recursive tree build method . |
33,694 | protected int maxDepth ( Layout . Node node ) { int depth = 0 ; for ( int i = 0 ; i < node . numChildren ( ) ; i ++ ) { depth = Math . max ( depth , maxDepth ( node . getChild ( i ) ) ) ; } return depth + 1 ; } | Compute the depth of the graph . |
33,695 | public void initialize ( ) { TreeIndexHeader header = createHeader ( ) ; if ( this . file . initialize ( header ) ) { initializeFromFile ( header , file ) ; } rootEntry = createRootEntry ( ) ; } | Initialize the tree if the page file already existed . |
33,696 | public N getNode ( int nodeID ) { if ( nodeID == getPageID ( rootEntry ) ) { return getRoot ( ) ; } else { return file . readPage ( nodeID ) ; } } | Returns the node with the specified id . |
33,697 | public void initializeFromFile ( TreeIndexHeader header , PageFile < N > file ) { this . dirCapacity = header . getDirCapacity ( ) ; this . leafCapacity = header . getLeafCapacity ( ) ; this . dirMinimum = header . getDirMinimum ( ) ; this . leafMinimum = header . getLeafMinimum ( ) ; if ( getLogger ( ) . isDebugging ( ) ) { StringBuilder msg = new StringBuilder ( ) ; msg . append ( getClass ( ) ) ; msg . append ( "\n file = " ) . append ( file . getClass ( ) ) ; getLogger ( ) . debugFine ( msg . toString ( ) ) ; } this . initialized = true ; } | Initializes this index from an existing persistent file . |
33,698 | protected final void initialize ( E exampleLeaf ) { initializeCapacities ( exampleLeaf ) ; createEmptyRoot ( exampleLeaf ) ; final Logging log = getLogger ( ) ; if ( log . isStatistics ( ) ) { String cls = this . getClass ( ) . getName ( ) ; log . statistics ( new LongStatistic ( cls + ".directory.capacity" , dirCapacity ) ) ; log . statistics ( new LongStatistic ( cls + ".directory.minfill" , dirMinimum ) ) ; log . statistics ( new LongStatistic ( cls + ".leaf.capacity" , leafCapacity ) ) ; log . statistics ( new LongStatistic ( cls + ".leaf.minfill" , leafMinimum ) ) ; } initialized = true ; } | Initializes the index . |
33,699 | public static MeanVarianceMinMax [ ] newArray ( int dimensionality ) { MeanVarianceMinMax [ ] arr = new MeanVarianceMinMax [ dimensionality ] ; for ( int i = 0 ; i < dimensionality ; i ++ ) { arr [ i ] = new MeanVarianceMinMax ( ) ; } return arr ; } | Create and initialize a new array of MeanVarianceMinMax |
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