idx int64 0 41.2k | question stringlengths 83 4.15k | target stringlengths 5 715 |
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33,500 | private static void drawLine ( int x0 , int y0 , int x1 , int y1 , boolean [ ] [ ] pic ) { final int xres = pic . length , yres = pic [ 0 ] . length ; y0 = ( y0 < 0 ) ? 0 : ( y0 >= yres ) ? ( yres - 1 ) : y0 ; y1 = ( y1 < 0 ) ? 0 : ( y1 >= yres ) ? ( yres - 1 ) : y1 ; x0 = ( x0 < 0 ) ? 0 : ( x0 >= xres ) ? ( xres - 1 ) : x0 ; x1 = ( x1 < 0 ) ? 0 : ( x1 >= xres ) ? ( xres - 1 ) : x1 ; final int dx = + Math . abs ( x1 - x0 ) , sx = x0 < x1 ? 1 : - 1 ; final int dy = - Math . abs ( y1 - y0 ) , sy = y0 < y1 ? 1 : - 1 ; int err = dx + dy ; for ( ; ; ) { pic [ x0 ] [ y0 ] = true ; if ( x0 == x1 && y0 == y1 ) { break ; } final int e2 = err << 1 ; if ( e2 > dy ) { err += dy ; x0 += sx ; } if ( e2 < dx ) { err += dx ; y0 += sy ; } } } | Draw a line onto the array using the classic Bresenham algorithm . |
33,501 | public Collection < String > getPossibleValues ( ) { final E [ ] enums = enumClass . getEnumConstants ( ) ; ArrayList < String > values = new ArrayList < > ( enums . length ) ; for ( E t : enums ) { values . add ( t . name ( ) ) ; } return values ; } | Get a list of possible values for this enum parameter . |
33,502 | private String joinEnumNames ( String separator ) { E [ ] enumTypes = enumClass . getEnumConstants ( ) ; StringBuilder sb = new StringBuilder ( ) ; for ( int i = 0 ; i < enumTypes . length ; ++ i ) { if ( i > 0 ) { sb . append ( separator ) ; } sb . append ( enumTypes [ i ] . name ( ) ) ; } return sb . toString ( ) ; } | Utility method for merging possible values into a string for informational messages . |
33,503 | protected void preInsert ( MkMaxEntry entry ) { KNNHeap knns_o = DBIDUtil . newHeap ( getKmax ( ) ) ; preInsert ( entry , getRootEntry ( ) , knns_o ) ; } | Adapts the knn distances before insertion of the specified entry . |
33,504 | public static IntIterator getCommonDimensions ( Collection < SplitHistory > splitHistories ) { Iterator < SplitHistory > it = splitHistories . iterator ( ) ; long [ ] checkSet = BitsUtil . copy ( it . next ( ) . dimBits ) ; while ( it . hasNext ( ) ) { SplitHistory sh = it . next ( ) ; BitsUtil . andI ( checkSet , sh . dimBits ) ; } return new BitsetIterator ( checkSet ) ; } | Get the common split dimensions from a list of split histories . |
33,505 | public static Filter handleURL ( ParsedURL url ) { if ( LOG . isDebuggingFiner ( ) ) { LOG . debugFiner ( "handleURL " + url . toString ( ) ) ; } if ( ! isCompatibleURLStatic ( url ) ) { return null ; } int id ; try { id = ParseUtil . parseIntBase10 ( url . getPath ( ) ) ; } catch ( NumberFormatException e ) { return null ; } SoftReference < RenderedImage > ref = images . get ( id ) ; if ( ref != null ) { RenderedImage ri = ref . get ( ) ; if ( ri == null ) { LOG . warning ( "Referenced image has expired from the cache!" ) ; } else { return new RedRable ( GraphicsUtil . wrap ( ri ) ) ; } } return null ; } | Statically handle the URL access . |
33,506 | public static int globalCentroid ( Centroid overallCentroid , Relation < ? extends NumberVector > rel , List < ? extends Cluster < ? > > clusters , NumberVector [ ] centroids , NoiseHandling noiseOption ) { int clustercount = 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 : continue ; case TREAT_NOISE_AS_SINGLETONS : clustercount += cluster . size ( ) ; for ( DBIDIter it = cluster . getIDs ( ) . iter ( ) ; it . valid ( ) ; it . advance ( ) ) { overallCentroid . put ( rel . get ( it ) ) ; } continue ; case MERGE_NOISE : break ; } } assert ( centroids [ i ] != null ) ; overallCentroid . put ( centroids [ i ] , cluster . size ( ) ) ; ++ clustercount ; } return clustercount ; } | Update the global centroid . |
33,507 | public DBID findPrototype ( DBIDs members ) { DBIDIter it = members . iter ( ) ; DBIDVar proto = DBIDUtil . newVar ( it ) , last = DBIDUtil . newVar ( it ) ; int maxprio = Integer . MIN_VALUE , secprio = Integer . MIN_VALUE ; for ( ; it . valid ( ) ; it . advance ( ) ) { int prio = mergeOrder . intValue ( it ) ; if ( prio > maxprio ) { secprio = maxprio ; proto . set ( last ) ; maxprio = prio ; last . set ( it ) ; } else if ( prio > secprio ) { secprio = prio ; proto . set ( it ) ; } } return DBIDUtil . deref ( prototypes . assignVar ( proto , proto ) ) ; } | Extract the prototype of a given cluster . When the argument is not a valid cluster of this Pointer Hierarchy the return value is unspecified . |
33,508 | public void bulkLoad ( DBIDs ids ) { if ( ids . size ( ) == 0 ) { return ; } assert ( root == null ) : "Tree already initialized." ; DBIDIter it = ids . iter ( ) ; DBID first = DBIDUtil . deref ( it ) ; ModifiableDoubleDBIDList candidates = DBIDUtil . newDistanceDBIDList ( ids . size ( ) - 1 ) ; for ( it . advance ( ) ; it . valid ( ) ; it . advance ( ) ) { candidates . add ( distance ( first , it ) , it ) ; } root = bulkConstruct ( first , Integer . MAX_VALUE , candidates ) ; } | Bulk - load the index . |
33,509 | private void checkCoverTree ( Node cur , int [ ] counts , int depth ) { counts [ 0 ] += 1 ; counts [ 1 ] += depth ; counts [ 2 ] = depth > counts [ 2 ] ? depth : counts [ 2 ] ; counts [ 3 ] += cur . singletons . size ( ) - 1 ; counts [ 4 ] += cur . singletons . size ( ) - ( cur . children == null ? 0 : 1 ) ; if ( cur . children != null ) { ++ depth ; for ( Node chi : cur . children ) { checkCoverTree ( chi , counts , depth ) ; } assert ( ! cur . children . isEmpty ( ) ) : "Empty childs list." ; } } | Collect some statistics on the tree . |
33,510 | protected void addToStatistics ( NumberVector nv ) { final int d = nv . getDimensionality ( ) ; assert ( d == ls . length ) ; this . n ++ ; for ( int i = 0 ; i < d ; i ++ ) { double v = nv . doubleValue ( i ) ; ls [ i ] += v ; ss += v * v ; } } | Add a number vector to the current node . |
33,511 | protected void addToStatistics ( ClusteringFeature other ) { n += other . n ; VMath . plusEquals ( ls , other . ls ) ; ss += other . ss ; } | Merge an other clustering features . |
33,512 | public double sumOfSquaresOfSums ( ) { double sum = 0. ; for ( int i = 0 ; i < ls . length ; i ++ ) { double v = ls [ i ] ; sum += v * v ; } return sum ; } | Sum over all dimensions of squares of linear sums . |
33,513 | public static double sumOfSquares ( NumberVector v ) { final int dim = v . getDimensionality ( ) ; double sum = 0 ; for ( int d = 0 ; d < dim ; d ++ ) { double x = v . doubleValue ( d ) ; sum += x * x ; } return sum ; } | Compute the sum of squares of a vector . |
33,514 | private static void insertionSort ( int [ ] data , final int start , final int end , IntComparator comp ) { for ( int i = start + 1 ; i < end ; i ++ ) { final int cur = data [ i ] ; int j = i - 1 ; while ( j >= start ) { final int pre = data [ j ] ; if ( comp . compare ( cur , pre ) >= 0 ) { break ; } data [ j + 1 ] = pre ; -- j ; } data [ j + 1 ] = cur ; } } | Insertion sort for short arrays . |
33,515 | public double [ ] [ ] processIds ( DBIDs ids , Relation < ? extends NumberVector > relation ) { final int dim = RelationUtil . dimensionality ( relation ) ; final CovarianceMatrix cmat = new CovarianceMatrix ( dim ) ; final Centroid centroid = Centroid . make ( relation , ids ) ; double maxdist = 0.0 , stddev = 0.0 ; { for ( DBIDIter iter = ids . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { double distance = weightDistance . distance ( centroid , relation . get ( iter ) ) ; stddev += distance * distance ; if ( distance > maxdist ) { maxdist = distance ; } } if ( maxdist == 0.0 ) { maxdist = 1.0 ; } stddev = FastMath . sqrt ( stddev / ids . size ( ) ) ; } for ( DBIDIter iter = ids . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { NumberVector obj = relation . get ( iter ) ; double distance = weightDistance . distance ( centroid , obj ) ; double weight = weightfunction . getWeight ( distance , maxdist , stddev ) ; cmat . put ( obj , weight ) ; } return cmat . destroyToPopulationMatrix ( ) ; } | Weighted Covariance Matrix for a set of IDs . Since we are not supplied any distance information we ll need to compute it ourselves . Covariance is tied to Euclidean distance so it probably does not make much sense to add support for other distance functions? |
33,516 | public double [ ] [ ] processQueryResults ( DoubleDBIDList results , Relation < ? extends NumberVector > database , int k ) { final int dim = RelationUtil . dimensionality ( database ) ; final CovarianceMatrix cmat = new CovarianceMatrix ( dim ) ; k = k <= results . size ( ) ? k : results . size ( ) ; double maxdist = 0.0 , stddev = 0.0 ; { int i = 0 ; for ( DoubleDBIDListIter it = results . iter ( ) ; it . valid ( ) && i < k ; it . advance ( ) , k ++ ) { final double dist = it . doubleValue ( ) ; stddev += dist * dist ; if ( dist > maxdist ) { maxdist = dist ; } } if ( maxdist == 0.0 ) { maxdist = 1.0 ; } stddev = FastMath . sqrt ( stddev / k ) ; } int i = 0 ; for ( DoubleDBIDListIter it = results . iter ( ) ; it . valid ( ) && i < k ; it . advance ( ) , k ++ ) { final double dist = it . doubleValue ( ) ; NumberVector obj = database . get ( it ) ; double weight = weightfunction . getWeight ( dist , maxdist , stddev ) ; cmat . put ( obj , weight ) ; } return cmat . destroyToPopulationMatrix ( ) ; } | Compute Covariance Matrix for a QueryResult Collection . |
33,517 | public Instance instantiate ( Database database , Relation < V > relation ) { DistanceQuery < V > dq = database . getDistanceQuery ( relation , EuclideanDistanceFunction . STATIC ) ; KNNQuery < V > knnq = database . getKNNQuery ( dq , settings . k ) ; WritableDataStore < PCAFilteredResult > storage = DataStoreUtil . makeStorage ( relation . getDBIDs ( ) , DataStoreFactory . HINT_HOT | DataStoreFactory . HINT_TEMP , PCAFilteredResult . class ) ; PCARunner pca = settings . pca ; EigenPairFilter filter = settings . filter ; Duration time = LOG . newDuration ( this . getClass ( ) . getName ( ) + ".preprocessing-time" ) . begin ( ) ; FiniteProgress progress = LOG . isVerbose ( ) ? new FiniteProgress ( this . getClass ( ) . getName ( ) , relation . size ( ) , LOG ) : null ; for ( DBIDIter iditer = relation . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { DoubleDBIDList ref = knnq . getKNNForDBID ( iditer , settings . k ) ; PCAResult pcares = pca . processQueryResult ( ref , relation ) ; storage . put ( iditer , new PCAFilteredResult ( pcares . getEigenPairs ( ) , filter . filter ( pcares . getEigenvalues ( ) ) , 1. , 0. ) ) ; LOG . incrementProcessed ( progress ) ; } LOG . ensureCompleted ( progress ) ; LOG . statistics ( time . end ( ) ) ; return new Instance ( relation . getDBIDs ( ) , storage , relation ) ; } | Full instantiation interface . |
33,518 | public static void writeXHTML ( Document htmldoc , OutputStream out ) throws IOException { javax . xml . transform . Result result = new StreamResult ( out ) ; Transformer xformer ; try { xformer = TransformerFactory . newInstance ( ) . newTransformer ( ) ; xformer . setOutputProperty ( OutputKeys . INDENT , "yes" ) ; xformer . setOutputProperty ( OutputKeys . ENCODING , "UTF-8" ) ; xformer . setOutputProperty ( OutputKeys . DOCTYPE_PUBLIC , HTML_XHTML_TRANSITIONAL_DOCTYPE_PUBLIC ) ; xformer . setOutputProperty ( OutputKeys . DOCTYPE_SYSTEM , HTML_XHTML_TRANSITIONAL_DOCTYPE_SYSTEM ) ; xformer . transform ( new DOMSource ( htmldoc ) , result ) ; } catch ( TransformerException e1 ) { throw new IOException ( e1 ) ; } out . flush ( ) ; } | Write an HTML document to an output stream . |
33,519 | public static Element appendMultilineText ( Document htmldoc , Element parent , String text ) { String [ ] parts = text != null ? text . split ( "\n" ) : null ; if ( parts == null || parts . length == 0 ) { return parent ; } parent . appendChild ( htmldoc . createTextNode ( parts [ 0 ] ) ) ; for ( int i = 1 ; i < parts . length ; i ++ ) { parent . appendChild ( htmldoc . createElement ( HTML_BR_TAG ) ) ; parent . appendChild ( htmldoc . createTextNode ( parts [ i ] ) ) ; } return parent ; } | Append a multiline text to a node transforming linewraps into BR tags . |
33,520 | public int getPathCount ( ) { int result = 0 ; for ( IndexTreePath < E > path = this ; path != null ; path = path . parentPath ) { result ++ ; } return result ; } | Returns the number of elements in the path . |
33,521 | public OutlierResult run ( Database database , Relation < O > relation ) { StepProgress stepprog = LOG . isVerbose ( ) ? new StepProgress ( "CBLOF" , 3 ) : null ; DBIDs ids = relation . getDBIDs ( ) ; LOG . beginStep ( stepprog , 1 , "Computing clustering." ) ; Clustering < MeanModel > clustering = clusteringAlgorithm . run ( database ) ; LOG . beginStep ( stepprog , 2 , "Computing boundary between large and small clusters." ) ; List < ? extends Cluster < MeanModel > > clusters = clustering . getAllClusters ( ) ; Collections . sort ( clusters , new Comparator < Cluster < MeanModel > > ( ) { public int compare ( Cluster < MeanModel > o1 , Cluster < MeanModel > o2 ) { return Integer . compare ( o2 . size ( ) , o1 . size ( ) ) ; } } ) ; int clusterBoundary = getClusterBoundary ( relation , clusters ) ; List < ? extends Cluster < MeanModel > > largeClusters = clusters . subList ( 0 , clusterBoundary + 1 ) ; List < ? extends Cluster < MeanModel > > smallClusters = clusters . subList ( clusterBoundary + 1 , clusters . size ( ) ) ; LOG . beginStep ( stepprog , 3 , "Computing Cluster-Based Local Outlier Factors (CBLOF)." ) ; WritableDoubleDataStore cblofs = DataStoreUtil . makeDoubleStorage ( ids , DataStoreFactory . HINT_HOT | DataStoreFactory . HINT_DB ) ; DoubleMinMax cblofMinMax = new DoubleMinMax ( ) ; computeCBLOFs ( relation , distance , cblofs , cblofMinMax , largeClusters , smallClusters ) ; LOG . setCompleted ( stepprog ) ; DoubleRelation scoreResult = new MaterializedDoubleRelation ( "Cluster-Based Local Outlier Factor" , "cblof-outlier" , cblofs , ids ) ; OutlierScoreMeta scoreMeta = new QuotientOutlierScoreMeta ( cblofMinMax . getMin ( ) , cblofMinMax . getMax ( ) , 0.0 , Double . POSITIVE_INFINITY , 1.0 ) ; return new OutlierResult ( scoreMeta , scoreResult ) ; } | Runs the CBLOF algorithm on the given database . |
33,522 | private int getClusterBoundary ( Relation < O > relation , List < ? extends Cluster < MeanModel > > clusters ) { int totalSize = relation . size ( ) ; int clusterBoundary = clusters . size ( ) - 1 ; int cumulativeSize = 0 ; for ( int i = 0 ; i < clusters . size ( ) - 1 ; i ++ ) { cumulativeSize += clusters . get ( i ) . size ( ) ; if ( cumulativeSize >= totalSize * alpha ) { clusterBoundary = i ; break ; } if ( clusters . get ( i ) . size ( ) / ( double ) clusters . get ( i + 1 ) . size ( ) >= beta ) { clusterBoundary = i ; break ; } } return clusterBoundary ; } | Compute the boundary index separating the large cluster from the small cluster . |
33,523 | private void computeCBLOFs ( Relation < O > relation , NumberVectorDistanceFunction < ? super O > distance , WritableDoubleDataStore cblofs , DoubleMinMax cblofMinMax , List < ? extends Cluster < MeanModel > > largeClusters , List < ? extends Cluster < MeanModel > > smallClusters ) { List < NumberVector > largeClusterMeans = new ArrayList < > ( largeClusters . size ( ) ) ; for ( Cluster < MeanModel > largeCluster : largeClusters ) { NumberVector mean = ModelUtil . getPrototypeOrCentroid ( largeCluster . getModel ( ) , relation , largeCluster . getIDs ( ) ) ; largeClusterMeans . add ( mean ) ; for ( DBIDIter iter = largeCluster . getIDs ( ) . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { double cblof = computeLargeClusterCBLOF ( relation . get ( iter ) , distance , mean , largeCluster ) ; storeCBLOFScore ( cblofs , cblofMinMax , cblof , iter ) ; } } for ( Cluster < MeanModel > smallCluster : smallClusters ) { for ( DBIDIter iter = smallCluster . getIDs ( ) . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { double cblof = computeSmallClusterCBLOF ( relation . get ( iter ) , distance , largeClusterMeans , smallCluster ) ; storeCBLOFScore ( cblofs , cblofMinMax , cblof , iter ) ; } } } | Compute the CBLOF scores for all the data . |
33,524 | private GeneratorMain loadXMLSpecification ( ) { try { DocumentBuilderFactory dbf = DocumentBuilderFactory . newInstance ( ) ; dbf . setFeature ( "http://apache.org/xml/features/nonvalidating/load-external-dtd" , false ) ; URL url = ClassLoader . getSystemResource ( GENERATOR_SCHEMA_FILE ) ; if ( url != null ) { try { Schema schema = SchemaFactory . newInstance ( XMLConstants . W3C_XML_SCHEMA_NS_URI ) . newSchema ( url ) ; dbf . setSchema ( schema ) ; dbf . setIgnoringElementContentWhitespace ( true ) ; } catch ( Exception e ) { LOG . warning ( "Could not set up XML Schema validation for specification file." , e ) ; } } else { LOG . warning ( "Could not set up XML Schema validation for specification file." ) ; } Document doc = dbf . newDocumentBuilder ( ) . parse ( specfile ) ; Node root = doc . getDocumentElement ( ) ; if ( TAG_DATASET . equals ( root . getNodeName ( ) ) ) { GeneratorMain gen = new GeneratorMain ( ) ; processElementDataset ( gen , root ) ; return gen ; } else { throw new AbortException ( "Experiment specification has incorrect document element: " + root . getNodeName ( ) ) ; } } catch ( FileNotFoundException e ) { throw new AbortException ( "Can't open specification file." , e ) ; } catch ( SAXException e ) { throw new AbortException ( "Error parsing specification file." , e ) ; } catch ( IOException e ) { throw new AbortException ( "IO Exception loading specification file." , e ) ; } catch ( ParserConfigurationException e ) { throw new AbortException ( "Parser Configuration Error" , e ) ; } } | Load the XML configuration file . |
33,525 | private void processElementDataset ( GeneratorMain gen , Node cur ) { String seedstr = ( ( Element ) cur ) . getAttribute ( ATTR_SEED ) ; if ( clusterRandom != RandomFactory . DEFAULT && seedstr != null && seedstr . length ( ) > 0 ) { clusterRandom = new RandomFactory ( ( long ) ( ParseUtil . parseIntBase10 ( seedstr ) * sizescale ) ) ; } String testmod = ( ( Element ) cur ) . getAttribute ( ATTR_TEST ) ; if ( testmod != null && testmod . length ( ) > 0 ) { testAgainstModel = Boolean . valueOf ( ParseUtil . parseIntBase10 ( testmod ) != 0 ) ; } XMLNodeIterator iter = new XMLNodeIterator ( cur . getFirstChild ( ) ) ; while ( iter . hasNext ( ) ) { Node child = iter . next ( ) ; if ( TAG_CLUSTER . equals ( child . getNodeName ( ) ) ) { processElementCluster ( gen , child ) ; } else if ( TAG_STATIC . equals ( child . getNodeName ( ) ) ) { processElementStatic ( gen , child ) ; } else if ( child . getNodeType ( ) == Node . ELEMENT_NODE ) { LOG . warning ( "Unknown element in XML specification file: " + child . getNodeName ( ) ) ; } } } | Process a dataset Element in the XML stream . |
33,526 | private void processElementCluster ( GeneratorMain gen , Node cur ) { int size = - 1 ; double overweight = 1.0 ; String sizestr = ( ( Element ) cur ) . getAttribute ( ATTR_SIZE ) ; if ( sizestr != null && sizestr . length ( ) > 0 ) { size = ( int ) ( ParseUtil . parseIntBase10 ( sizestr ) * sizescale ) ; } String name = ( ( Element ) cur ) . getAttribute ( ATTR_NAME ) ; String dcostr = ( ( Element ) cur ) . getAttribute ( ATTR_DENSITY ) ; if ( dcostr != null && dcostr . length ( ) > 0 ) { overweight = ParseUtil . parseDouble ( dcostr ) ; } if ( size < 0 ) { throw new AbortException ( "No valid cluster size given in specification file." ) ; } if ( name == null || name . length ( ) == 0 ) { throw new AbortException ( "No cluster name given in specification file." ) ; } Random newRand = clusterRandom . getSingleThreadedRandom ( ) ; GeneratorSingleCluster cluster = new GeneratorSingleCluster ( name , size , overweight , newRand ) ; XMLNodeIterator iter = new XMLNodeIterator ( cur . getFirstChild ( ) ) ; while ( iter . hasNext ( ) ) { Node child = iter . next ( ) ; if ( TAG_UNIFORM . equals ( child . getNodeName ( ) ) ) { processElementUniform ( cluster , child ) ; } else if ( TAG_NORMAL . equals ( child . getNodeName ( ) ) ) { processElementNormal ( cluster , child ) ; } else if ( TAG_GAMMA . equals ( child . getNodeName ( ) ) ) { processElementGamma ( cluster , child ) ; } else if ( TAG_HALTON . equals ( child . getNodeName ( ) ) ) { processElementHalton ( cluster , child ) ; } else if ( TAG_ROTATE . equals ( child . getNodeName ( ) ) ) { processElementRotate ( cluster , child ) ; } else if ( TAG_TRANSLATE . equals ( child . getNodeName ( ) ) ) { processElementTranslate ( cluster , child ) ; } else if ( TAG_CLIP . equals ( child . getNodeName ( ) ) ) { processElementClipping ( cluster , child ) ; } else if ( child . getNodeType ( ) == Node . ELEMENT_NODE ) { LOG . warning ( "Unknown element in XML specification file: " + child . getNodeName ( ) ) ; } } gen . addCluster ( cluster ) ; } | Process a cluster Element in the XML stream . |
33,527 | private void processElementUniform ( GeneratorSingleCluster cluster , Node cur ) { double min = 0.0 ; double max = 1.0 ; String minstr = ( ( Element ) cur ) . getAttribute ( ATTR_MIN ) ; if ( minstr != null && minstr . length ( ) > 0 ) { min = ParseUtil . parseDouble ( minstr ) ; } String maxstr = ( ( Element ) cur ) . getAttribute ( ATTR_MAX ) ; if ( maxstr != null && maxstr . length ( ) > 0 ) { max = ParseUtil . parseDouble ( maxstr ) ; } Random random = cluster . getNewRandomGenerator ( ) ; Distribution generator = new UniformDistribution ( min , max , random ) ; cluster . addGenerator ( generator ) ; XMLNodeIterator iter = new XMLNodeIterator ( cur . getFirstChild ( ) ) ; while ( iter . hasNext ( ) ) { Node child = iter . next ( ) ; if ( child . getNodeType ( ) == Node . ELEMENT_NODE ) { LOG . warning ( "Unknown element in XML specification file: " + child . getNodeName ( ) ) ; } } } | Process a uniform Element in the XML stream . |
33,528 | private void processElementNormal ( GeneratorSingleCluster cluster , Node cur ) { double mean = 0.0 ; double stddev = 1.0 ; String meanstr = ( ( Element ) cur ) . getAttribute ( ATTR_MEAN ) ; if ( meanstr != null && meanstr . length ( ) > 0 ) { mean = ParseUtil . parseDouble ( meanstr ) ; } String stddevstr = ( ( Element ) cur ) . getAttribute ( ATTR_STDDEV ) ; if ( stddevstr != null && stddevstr . length ( ) > 0 ) { stddev = ParseUtil . parseDouble ( stddevstr ) ; } Random random = cluster . getNewRandomGenerator ( ) ; Distribution generator = new NormalDistribution ( mean , stddev , random ) ; cluster . addGenerator ( generator ) ; XMLNodeIterator iter = new XMLNodeIterator ( cur . getFirstChild ( ) ) ; while ( iter . hasNext ( ) ) { Node child = iter . next ( ) ; if ( child . getNodeType ( ) == Node . ELEMENT_NODE ) { LOG . warning ( "Unknown element in XML specification file: " + child . getNodeName ( ) ) ; } } } | Process a normal Element in the XML stream . |
33,529 | private void processElementGamma ( GeneratorSingleCluster cluster , Node cur ) { double k = 1.0 ; double theta = 1.0 ; String kstr = ( ( Element ) cur ) . getAttribute ( ATTR_K ) ; if ( kstr != null && kstr . length ( ) > 0 ) { k = ParseUtil . parseDouble ( kstr ) ; } String thetastr = ( ( Element ) cur ) . getAttribute ( ATTR_THETA ) ; if ( thetastr != null && thetastr . length ( ) > 0 ) { theta = ParseUtil . parseDouble ( thetastr ) ; } Random random = cluster . getNewRandomGenerator ( ) ; Distribution generator = new GammaDistribution ( k , theta , random ) ; cluster . addGenerator ( generator ) ; XMLNodeIterator iter = new XMLNodeIterator ( cur . getFirstChild ( ) ) ; while ( iter . hasNext ( ) ) { Node child = iter . next ( ) ; if ( child . getNodeType ( ) == Node . ELEMENT_NODE ) { LOG . warning ( "Unknown element in XML specification file: " + child . getNodeName ( ) ) ; } } } | Process a gamma Element in the XML stream . |
33,530 | private void processElementRotate ( GeneratorSingleCluster cluster , Node cur ) { int axis1 = 0 ; int axis2 = 0 ; double angle = 0.0 ; String a1str = ( ( Element ) cur ) . getAttribute ( ATTR_AXIS1 ) ; if ( a1str != null && a1str . length ( ) > 0 ) { axis1 = ParseUtil . parseIntBase10 ( a1str ) ; } String a2str = ( ( Element ) cur ) . getAttribute ( ATTR_AXIS2 ) ; if ( a2str != null && a2str . length ( ) > 0 ) { axis2 = ParseUtil . parseIntBase10 ( a2str ) ; } String anstr = ( ( Element ) cur ) . getAttribute ( ATTR_ANGLE ) ; if ( anstr != null && anstr . length ( ) > 0 ) { angle = ParseUtil . parseDouble ( anstr ) ; } if ( axis1 <= 0 || axis1 > cluster . getDim ( ) ) { throw new AbortException ( "Invalid axis1 number given in specification file." ) ; } if ( axis2 <= 0 || axis2 > cluster . getDim ( ) ) { throw new AbortException ( "Invalid axis2 number given in specification file." ) ; } if ( axis1 == axis2 ) { throw new AbortException ( "Invalid axis numbers given in specification file." ) ; } cluster . addRotation ( axis1 - 1 , axis2 - 1 , Math . toRadians ( angle ) ) ; XMLNodeIterator iter = new XMLNodeIterator ( cur . getFirstChild ( ) ) ; while ( iter . hasNext ( ) ) { Node child = iter . next ( ) ; if ( child . getNodeType ( ) == Node . ELEMENT_NODE ) { LOG . warning ( "Unknown element in XML specification file: " + child . getNodeName ( ) ) ; } } } | Process a rotate Element in the XML stream . |
33,531 | private void processElementTranslate ( GeneratorSingleCluster cluster , Node cur ) { double [ ] offset = null ; String vstr = ( ( Element ) cur ) . getAttribute ( ATTR_VECTOR ) ; if ( vstr != null && vstr . length ( ) > 0 ) { offset = parseVector ( vstr ) ; } if ( offset == null ) { throw new AbortException ( "No translation vector given." ) ; } cluster . addTranslation ( offset ) ; XMLNodeIterator iter = new XMLNodeIterator ( cur . getFirstChild ( ) ) ; while ( iter . hasNext ( ) ) { Node child = iter . next ( ) ; if ( child . getNodeType ( ) == Node . ELEMENT_NODE ) { LOG . warning ( "Unknown element in XML specification file: " + child . getNodeName ( ) ) ; } } } | Process a translate Element in the XML stream . |
33,532 | private void processElementClipping ( GeneratorSingleCluster cluster , Node cur ) { double [ ] cmin = null , cmax = null ; String minstr = ( ( Element ) cur ) . getAttribute ( ATTR_MIN ) ; if ( minstr != null && minstr . length ( ) > 0 ) { cmin = parseVector ( minstr ) ; } String maxstr = ( ( Element ) cur ) . getAttribute ( ATTR_MAX ) ; if ( maxstr != null && maxstr . length ( ) > 0 ) { cmax = parseVector ( maxstr ) ; } if ( cmin == null || cmax == null ) { throw new AbortException ( "No or incomplete clipping vectors given." ) ; } cluster . setClipping ( cmin , cmax ) ; XMLNodeIterator iter = new XMLNodeIterator ( cur . getFirstChild ( ) ) ; while ( iter . hasNext ( ) ) { Node child = iter . next ( ) ; if ( child . getNodeType ( ) == Node . ELEMENT_NODE ) { LOG . warning ( "Unknown element in XML specification file: " + child . getNodeName ( ) ) ; } } } | Process a clipping Element in the XML stream . |
33,533 | private void processElementStatic ( GeneratorMain gen , Node cur ) { String name = ( ( Element ) cur ) . getAttribute ( ATTR_NAME ) ; if ( name == null ) { throw new AbortException ( "No cluster name given in specification file." ) ; } ArrayList < double [ ] > points = new ArrayList < > ( ) ; XMLNodeIterator iter = new XMLNodeIterator ( cur . getFirstChild ( ) ) ; while ( iter . hasNext ( ) ) { Node child = iter . next ( ) ; if ( TAG_POINT . equals ( child . getNodeName ( ) ) ) { processElementPoint ( points , child ) ; } else if ( child . getNodeType ( ) == Node . ELEMENT_NODE ) { LOG . warning ( "Unknown element in XML specification file: " + child . getNodeName ( ) ) ; } } GeneratorStatic cluster = new GeneratorStatic ( name , points ) ; gen . addCluster ( cluster ) ; if ( LOG . isVerbose ( ) ) { LOG . verbose ( "Loaded cluster " + cluster . name + " from specification." ) ; } } | Process a static cluster Element in the XML stream . |
33,534 | private double [ ] parseVector ( String s ) { String [ ] entries = WHITESPACE_PATTERN . split ( s ) ; double [ ] d = new double [ entries . length ] ; for ( int i = 0 ; i < entries . length ; i ++ ) { try { d [ i ] = ParseUtil . parseDouble ( entries [ i ] ) ; } catch ( NumberFormatException e ) { throw new AbortException ( "Could not parse vector." ) ; } } return d ; } | Parse a string into a vector . |
33,535 | public static NumberVector getPrototype ( Model model , Relation < ? extends NumberVector > relation ) { if ( model instanceof MeanModel ) { return DoubleVector . wrap ( ( ( MeanModel ) model ) . getMean ( ) ) ; } if ( model instanceof MedoidModel ) { return relation . get ( ( ( MedoidModel ) model ) . getMedoid ( ) ) ; } if ( model instanceof PrototypeModel ) { Object p = ( ( PrototypeModel < ? > ) model ) . getPrototype ( ) ; if ( p instanceof NumberVector ) { return ( NumberVector ) p ; } return null ; } return null ; } | Get the representative vector for a cluster model . |
33,536 | private long [ ] determinePreferenceVectorByApriori ( Relation < V > relation , ModifiableDBIDs [ ] neighborIDs , StringBuilder msg ) { int dimensionality = neighborIDs . length ; UpdatableDatabase apriori_db = new HashmapDatabase ( ) ; SimpleTypeInformation < ? > bitmeta = VectorFieldTypeInformation . typeRequest ( BitVector . class , dimensionality , dimensionality ) ; for ( DBIDIter it = relation . iterDBIDs ( ) ; it . valid ( ) ; it . advance ( ) ) { long [ ] bits = BitsUtil . zero ( dimensionality ) ; boolean allFalse = true ; for ( int d = 0 ; d < dimensionality ; d ++ ) { if ( neighborIDs [ d ] . contains ( it ) ) { BitsUtil . setI ( bits , d ) ; allFalse = false ; } } if ( ! allFalse ) { SingleObjectBundle oaa = new SingleObjectBundle ( ) ; oaa . append ( bitmeta , new BitVector ( bits , dimensionality ) ) ; apriori_db . insert ( oaa ) ; } } APRIORI apriori = new APRIORI ( minpts ) ; FrequentItemsetsResult aprioriResult = apriori . run ( apriori_db ) ; List < Itemset > frequentItemsets = aprioriResult . getItemsets ( ) ; if ( msg != null ) { msg . append ( "\n Frequent itemsets: " ) . append ( frequentItemsets ) ; } int maxSupport = 0 , maxCardinality = 0 ; long [ ] preferenceVector = BitsUtil . zero ( dimensionality ) ; for ( Itemset itemset : frequentItemsets ) { if ( ( maxCardinality < itemset . length ( ) ) || ( maxCardinality == itemset . length ( ) && maxSupport == itemset . getSupport ( ) ) ) { preferenceVector = Itemset . toBitset ( itemset , BitsUtil . zero ( dimensionality ) ) ; maxCardinality = itemset . length ( ) ; maxSupport = itemset . getSupport ( ) ; } } if ( msg != null ) { msg . append ( "\n preference " ) . append ( BitsUtil . toStringLow ( preferenceVector , dimensionality ) ) . append ( '\n' ) ; LOG . debugFine ( msg . toString ( ) ) ; } return preferenceVector ; } | Determines the preference vector with the apriori strategy . |
33,537 | private long [ ] determinePreferenceVectorByMaxIntersection ( ModifiableDBIDs [ ] neighborIDs , StringBuilder msg ) { int dimensionality = neighborIDs . length ; long [ ] preferenceVector = BitsUtil . zero ( dimensionality ) ; Map < Integer , ModifiableDBIDs > candidates = new HashMap < > ( dimensionality ) ; for ( int i = 0 ; i < dimensionality ; i ++ ) { ModifiableDBIDs s_i = neighborIDs [ i ] ; if ( s_i . size ( ) > minpts ) { candidates . put ( i , s_i ) ; } } if ( msg != null ) { msg . append ( "\n candidates " ) . append ( candidates . keySet ( ) ) ; } if ( ! candidates . isEmpty ( ) ) { int i = max ( candidates ) ; ModifiableDBIDs intersection = candidates . remove ( i ) ; BitsUtil . setI ( preferenceVector , i ) ; while ( ! candidates . isEmpty ( ) ) { i = maxIntersection ( candidates , intersection ) ; candidates . remove ( i ) ; if ( intersection . size ( ) < minpts ) { break ; } BitsUtil . setI ( preferenceVector , i ) ; } } if ( msg != null ) { msg . append ( "\n preference " ) . append ( BitsUtil . toStringLow ( preferenceVector , dimensionality ) ) ; LOG . debug ( msg . toString ( ) ) ; } return preferenceVector ; } | Determines the preference vector with the max intersection strategy . |
33,538 | private int max ( Map < Integer , ModifiableDBIDs > candidates ) { int maxDim = - 1 , size = - 1 ; for ( Integer nextDim : candidates . keySet ( ) ) { int nextSet = candidates . get ( nextDim ) . size ( ) ; if ( size < nextSet ) { size = nextSet ; maxDim = nextDim ; } } return maxDim ; } | Returns the set with the maximum size contained in the specified map . |
33,539 | private int maxIntersection ( Map < Integer , ModifiableDBIDs > candidates , ModifiableDBIDs set ) { int maxDim = - 1 ; ModifiableDBIDs maxIntersection = null ; for ( Integer nextDim : candidates . keySet ( ) ) { DBIDs nextSet = candidates . get ( nextDim ) ; ModifiableDBIDs nextIntersection = DBIDUtil . intersection ( set , nextSet ) ; if ( maxDim < 0 || maxIntersection . size ( ) < nextIntersection . size ( ) ) { maxIntersection = nextIntersection ; maxDim = nextDim ; } } if ( maxDim >= 0 ) { set . clear ( ) ; set . addDBIDs ( maxIntersection ) ; } return maxDim ; } | Returns the index of the set having the maximum intersection set with the specified set contained in the specified map . |
33,540 | private RangeQuery < V > [ ] initRangeQueries ( Relation < V > relation , int dimensionality ) { @ SuppressWarnings ( "unchecked" ) RangeQuery < V > [ ] rangeQueries = ( RangeQuery < V > [ ] ) new RangeQuery [ dimensionality ] ; for ( int d = 0 ; d < dimensionality ; d ++ ) { rangeQueries [ d ] = relation . getRangeQuery ( new PrimitiveDistanceQuery < > ( relation , new OnedimensionalDistanceFunction ( d ) ) ) ; } return rangeQueries ; } | Initializes the dimension selecting distancefunctions to determine the preference vectors . |
33,541 | @ Reference ( authors = "F. J. Rohlf" , title = "Methods of comparing classifications" , booktitle = "Annual Review of Ecology and Systematics" , url = "https://doi.org/10.1146/annurev.es.05.110174.000533" , bibkey = "doi:10.1146/annurev.es.05.110174.000533" ) public double computeTau ( long c , long d , double m , long wd , long bd ) { double tie = ( wd * ( wd - 1 ) + bd * ( bd - 1 ) ) >>> 1 ; return ( c - d ) / FastMath . sqrt ( ( m - tie ) * m ) ; } | Compute the Tau correlation measure |
33,542 | public void checkServices ( String update ) { TreeSet < String > props = new TreeSet < > ( ) ; Enumeration < URL > us ; try { us = getClass ( ) . getClassLoader ( ) . getResources ( ELKIServiceLoader . RESOURCE_PREFIX ) ; } catch ( IOException e ) { throw new AbortException ( "Error enumerating service folders." , e ) ; } while ( us . hasMoreElements ( ) ) { URL u = us . nextElement ( ) ; try { if ( ( "jar" . equals ( u . getProtocol ( ) ) ) ) { JarURLConnection con = ( JarURLConnection ) u . openConnection ( ) ; try ( JarFile jar = con . getJarFile ( ) ) { Enumeration < JarEntry > entries = jar . entries ( ) ; while ( entries . hasMoreElements ( ) ) { String prop = entries . nextElement ( ) . getName ( ) ; if ( prop . startsWith ( ELKIServiceLoader . RESOURCE_PREFIX ) ) { props . add ( prop . substring ( ELKIServiceLoader . RESOURCE_PREFIX . length ( ) ) ) ; } else if ( prop . startsWith ( ELKIServiceLoader . FILENAME_PREFIX ) ) { props . add ( prop . substring ( ELKIServiceLoader . FILENAME_PREFIX . length ( ) ) ) ; } } } continue ; } if ( "file" . equals ( u . getProtocol ( ) ) ) { props . addAll ( Arrays . asList ( new File ( u . toURI ( ) ) . list ( ) ) ) ; } } catch ( IOException | URISyntaxException e ) { throw new AbortException ( "Error enumerating service folders." , e ) ; } } for ( String prop : props ) { if ( LOG . isVerbose ( ) ) { LOG . verbose ( "Checking property: " + prop ) ; } checkService ( prop , update ) ; } } | Retrieve all properties and check them . |
33,543 | @ SuppressWarnings ( "unchecked" ) private void checkAliases ( Class < ? > parent , String classname , String [ ] parts ) { Class < ? > c = ELKIServiceRegistry . findImplementation ( ( Class < Object > ) parent , classname ) ; if ( c == null ) { return ; } Alias ann = c . getAnnotation ( Alias . class ) ; if ( ann == null ) { if ( parts . length > 1 ) { StringBuilder buf = new StringBuilder ( 100 ) . append ( "Class " ) . append ( classname ) . append ( " in " ) . append ( parent . getCanonicalName ( ) ) . append ( " has the following extraneous aliases:" ) ; for ( int i = 1 ; i < parts . length ; i ++ ) { buf . append ( ' ' ) . append ( parts [ i ] ) ; } LOG . warning ( buf ) ; } return ; } HashSet < String > aliases = new HashSet < String > ( ) ; for ( int i = 1 ; i < parts . length ; i ++ ) { aliases . add ( parts [ i ] ) ; } StringBuilder buf = null ; for ( String a : ann . value ( ) ) { if ( ! aliases . remove ( a ) ) { if ( buf == null ) { buf = new StringBuilder ( 100 ) . append ( "Class " ) . append ( classname ) . append ( " in " ) . append ( parent . getCanonicalName ( ) ) . append ( " is missing the following aliases:" ) ; } buf . append ( ' ' ) . append ( a ) ; } } if ( ! aliases . isEmpty ( ) ) { buf = ( buf == null ? new StringBuilder ( ) : buf . append ( FormatUtil . NEWLINE ) ) . append ( "Class " ) . append ( classname ) . append ( " in " ) . append ( parent . getCanonicalName ( ) ) . append ( " has the following extraneous aliases:" ) ; for ( String a : aliases ) { buf . append ( ' ' ) . append ( a ) ; } } if ( buf != null ) { LOG . warning ( buf ) ; } } | Check if aliases are listed completely . |
33,544 | public LinearEquationSystem getNormalizedLinearEquationSystem ( Normalization < ? > normalization ) throws NonNumericFeaturesException { if ( normalization != null ) { LinearEquationSystem lq = normalization . transform ( linearEquationSystem ) ; lq . solveByTotalPivotSearch ( ) ; return lq ; } else { return linearEquationSystem ; } } | Returns the linear equation system for printing purposes . If normalization is null the linear equation system is returned otherwise the linear equation system will be transformed according to the normalization . |
33,545 | public double squaredDistance ( V p ) { double [ ] p_minus_a = minusEquals ( p . toArray ( ) , centroid ) ; return squareSum ( minusEquals ( p_minus_a , times ( strongEigenvectors , transposeTimes ( strongEigenvectors , p_minus_a ) ) ) ) ; } | Returns the distance of NumberVector p from the hyperplane underlying this solution . |
33,546 | public double [ ] errorVector ( V p ) { return weakEigenvectors . length > 0 ? times ( weakEigenvectors , transposeTimes ( weakEigenvectors , minusEquals ( p . toArray ( ) , centroid ) ) ) : EMPTY_VECTOR ; } | Returns the error vectors after projection . |
33,547 | public double [ ] dataVector ( V p ) { return strongEigenvectors . length > 0 ? times ( strongEigenvectors , transposeTimes ( strongEigenvectors , minusEquals ( p . toArray ( ) , centroid ) ) ) : EMPTY_VECTOR ; } | Returns the data vectors after projection . |
33,548 | public void writeToText ( TextWriterStream out , String label ) { if ( label != null ) { out . commentPrintLn ( label ) ; } out . commentPrintLn ( "Model class: " + this . getClass ( ) . getName ( ) ) ; try { if ( getNormalizedLinearEquationSystem ( null ) != null ) { LinearEquationSystem lq = getNormalizedLinearEquationSystem ( null ) ; out . commentPrint ( "Linear Equation System: " ) ; out . commentPrintLn ( lq . equationsToString ( nf ) ) ; } } catch ( NonNumericFeaturesException e ) { LoggingUtil . exception ( e ) ; } } | Text output of the equation system |
33,549 | protected int getBinNr ( double coord ) { if ( Double . isInfinite ( coord ) || Double . isNaN ( coord ) ) { throw new UnsupportedOperationException ( "Encountered non-finite value in Histogram: " + coord ) ; } if ( coord == max ) { return size - 1 ; } return ( int ) Math . floor ( ( coord - base ) / binsize ) + offset ; } | Compute the bin number . Has a special case for rounding max down to the last bin . |
33,550 | protected static int growSize ( int current , int requiredSize ) { int newCapacity = ( ( current < 64 ) ? ( ( current + 1 ) << 1 ) : ( ( current >> 1 ) * 3 ) ) ; if ( newCapacity < 0 ) { throw new OutOfMemoryError ( ) ; } if ( requiredSize > newCapacity ) { newCapacity = requiredSize ; } return requiredSize ; } | Compute the size to grow to . |
33,551 | private void expandNodes ( DeLiCluTree index , SpatialPrimitiveDistanceFunction < V > distFunction , SpatialObjectPair nodePair , DataStore < KNNList > knns ) { DeLiCluNode node1 = index . getNode ( ( ( SpatialDirectoryEntry ) nodePair . entry1 ) . getPageID ( ) ) ; DeLiCluNode node2 = index . getNode ( ( ( SpatialDirectoryEntry ) nodePair . entry2 ) . getPageID ( ) ) ; if ( node1 . isLeaf ( ) ) { expandLeafNodes ( distFunction , node1 , node2 , knns ) ; } else { expandDirNodes ( distFunction , node1 , node2 ) ; } index . setExpanded ( nodePair . entry2 , nodePair . entry1 ) ; } | Expands the spatial nodes of the specified pair . |
33,552 | private void expandDirNodes ( SpatialPrimitiveDistanceFunction < V > distFunction , DeLiCluNode node1 , DeLiCluNode node2 ) { if ( LOG . isDebuggingFinest ( ) ) { LOG . debugFinest ( "ExpandDirNodes: " + node1 . getPageID ( ) + " + " + node2 . getPageID ( ) ) ; } int numEntries_1 = node1 . getNumEntries ( ) ; int numEntries_2 = node2 . getNumEntries ( ) ; for ( int i = 0 ; i < numEntries_1 ; i ++ ) { DeLiCluEntry entry1 = node1 . getEntry ( i ) ; if ( ! entry1 . hasUnhandled ( ) ) { continue ; } for ( int j = 0 ; j < numEntries_2 ; j ++ ) { DeLiCluEntry entry2 = node2 . getEntry ( j ) ; if ( ! entry2 . hasHandled ( ) ) { continue ; } double distance = distFunction . minDist ( entry1 , entry2 ) ; heap . add ( new SpatialObjectPair ( distance , entry1 , entry2 , true ) ) ; } } } | Expands the specified directory nodes . |
33,553 | private void expandLeafNodes ( SpatialPrimitiveDistanceFunction < V > distFunction , DeLiCluNode node1 , DeLiCluNode node2 , DataStore < KNNList > knns ) { if ( LOG . isDebuggingFinest ( ) ) { LOG . debugFinest ( "ExpandLeafNodes: " + node1 . getPageID ( ) + " + " + node2 . getPageID ( ) ) ; } int numEntries_1 = node1 . getNumEntries ( ) ; int numEntries_2 = node2 . getNumEntries ( ) ; for ( int i = 0 ; i < numEntries_1 ; i ++ ) { DeLiCluEntry entry1 = node1 . getEntry ( i ) ; if ( ! entry1 . hasUnhandled ( ) ) { continue ; } for ( int j = 0 ; j < numEntries_2 ; j ++ ) { DeLiCluEntry entry2 = node2 . getEntry ( j ) ; if ( ! entry2 . hasHandled ( ) ) { continue ; } double distance = distFunction . minDist ( entry1 , entry2 ) ; double reach = MathUtil . max ( distance , knns . get ( ( ( LeafEntry ) entry2 ) . getDBID ( ) ) . getKNNDistance ( ) ) ; heap . add ( new SpatialObjectPair ( reach , entry1 , entry2 , false ) ) ; } } } | Expands the specified leaf nodes . |
33,554 | private void reinsertExpanded ( SpatialPrimitiveDistanceFunction < V > distFunction , DeLiCluTree index , IndexTreePath < DeLiCluEntry > path , DataStore < KNNList > knns ) { int l = 0 ; for ( IndexTreePath < DeLiCluEntry > it = path ; it != null ; it = it . getParentPath ( ) ) { l ++ ; } ArrayList < IndexTreePath < DeLiCluEntry > > p = new ArrayList < > ( l - 1 ) ; IndexTreePath < DeLiCluEntry > it = path ; for ( ; it . getParentPath ( ) != null ; it = it . getParentPath ( ) ) { p . add ( it ) ; } assert ( p . size ( ) == l - 1 ) ; DeLiCluEntry rootEntry = it . getEntry ( ) ; reinsertExpanded ( distFunction , index , p , l - 2 , rootEntry , knns ) ; } | Reinserts the objects of the already expanded nodes . |
33,555 | public static int assertSameDimensionality ( SpatialComparable box1 , SpatialComparable box2 ) { final int dim = box1 . getDimensionality ( ) ; if ( dim != box2 . getDimensionality ( ) ) { throw new IllegalArgumentException ( "The spatial objects do not have the same dimensionality!" ) ; } return dim ; } | Check that two spatial objects have the same dimensionality . |
33,556 | public static double [ ] getMin ( SpatialComparable box ) { final int dim = box . getDimensionality ( ) ; double [ ] min = new double [ dim ] ; for ( int i = 0 ; i < dim ; i ++ ) { min [ i ] = box . getMin ( i ) ; } return min ; } | Returns a clone of the minimum hyper point . |
33,557 | public static double [ ] getMax ( SpatialComparable box ) { final int dim = box . getDimensionality ( ) ; double [ ] max = new double [ dim ] ; for ( int i = 0 ; i < dim ; i ++ ) { max [ i ] = box . getMax ( i ) ; } return max ; } | Returns a clone of the maximum hyper point . |
33,558 | public static boolean intersects ( SpatialComparable box1 , SpatialComparable box2 ) { final int dim = assertSameDimensionality ( box1 , box2 ) ; for ( int i = 0 ; i < dim ; i ++ ) { if ( box2 . getMax ( i ) < box1 . getMin ( i ) || box1 . getMax ( i ) < box2 . getMin ( i ) ) { return false ; } } return true ; } | Returns true if the two SpatialComparables intersect false otherwise . |
33,559 | public static boolean contains ( SpatialComparable box , double [ ] point ) { final int dim = box . getDimensionality ( ) ; if ( dim != point . length ) { throw new IllegalArgumentException ( "This HyperBoundingBox and the given point need same dimensionality" ) ; } for ( int i = 0 ; i < dim ; i ++ ) { if ( box . getMin ( i ) > point [ i ] || box . getMax ( i ) < point [ i ] ) { return false ; } } return true ; } | Returns true if this SpatialComparable contains the given point false otherwise . |
33,560 | public static double enlargement ( SpatialComparable exist , SpatialComparable addit ) { final int dim = assertSameDimensionality ( exist , addit ) ; double v1 = 1. ; double v2 = 1. ; for ( int i = 0 ; i < dim ; i ++ ) { final double emin = exist . getMin ( i ) ; final double emax = exist . getMax ( i ) ; final double amin = addit . getMin ( i ) ; final double amax = addit . getMax ( i ) ; final double min = Math . min ( emin , amin ) ; final double max = Math . max ( emax , amax ) ; v1 *= ( max - min ) ; v2 *= ( emax - emin ) ; } return v2 - v1 ; } | Compute the enlargement obtained by adding an object to an existing object . |
33,561 | public static double perimeter ( SpatialComparable box ) { final int dim = box . getDimensionality ( ) ; double perimeter = 0. ; for ( int i = 0 ; i < dim ; i ++ ) { perimeter += box . getMax ( i ) - box . getMin ( i ) ; } return perimeter ; } | Computes the perimeter of this SpatialComparable . |
33,562 | public static double overlap ( SpatialComparable box1 , SpatialComparable box2 ) { final int dim = assertSameDimensionality ( box1 , box2 ) ; double omax , omin ; double overlap = 1. ; for ( int i = 0 ; i < dim ; i ++ ) { omax = Math . min ( box1 . getMax ( i ) , box2 . getMax ( i ) ) ; omin = Math . max ( box1 . getMin ( i ) , box2 . getMin ( i ) ) ; if ( omax <= omin ) { return 0. ; } overlap *= omax - omin ; } return overlap ; } | Computes the volume of the overlapping box between two SpatialComparables . |
33,563 | public static double relativeOverlap ( SpatialComparable box1 , SpatialComparable box2 ) { final int dim = assertSameDimensionality ( box1 , box2 ) ; double overlap = 1. ; double vol1 = 1. ; double vol2 = 1. ; for ( int i = 0 ; i < dim ; i ++ ) { final double box1min = box1 . getMin ( i ) ; final double box1max = box1 . getMax ( i ) ; final double box2min = box2 . getMin ( i ) ; final double box2max = box2 . getMax ( i ) ; final double omax = Math . min ( box1max , box2max ) ; final double omin = Math . max ( box1min , box2min ) ; if ( omax <= omin ) { return 0. ; } overlap *= omax - omin ; vol1 *= box1max - box1min ; vol2 *= box2max - box2min ; } return overlap / ( vol1 + vol2 ) ; } | Computes the volume of the overlapping box between two SpatialComparables and return the relation between the volume of the overlapping box and the volume of both SpatialComparable . |
33,564 | public static ModifiableHyperBoundingBox unionTolerant ( SpatialComparable mbr1 , SpatialComparable mbr2 ) { if ( mbr1 == null && mbr2 == null ) { return null ; } if ( mbr1 == null ) { return new ModifiableHyperBoundingBox ( mbr2 ) ; } if ( mbr2 == null ) { return new ModifiableHyperBoundingBox ( mbr1 ) ; } return union ( mbr1 , mbr2 ) ; } | Returns the union of the two specified MBRs . Tolerant of null values . |
33,565 | public static double [ ] centroid ( SpatialComparable obj ) { final int dim = obj . getDimensionality ( ) ; double [ ] centroid = new double [ dim ] ; for ( int d = 0 ; d < dim ; d ++ ) { centroid [ d ] = ( obj . getMax ( d ) + obj . getMin ( d ) ) * .5 ; } return centroid ; } | Returns the centroid of this SpatialComparable . |
33,566 | public static LinearScaling fromMinMax ( double min , double max ) { double zoom = 1.0 / ( max - min ) ; return new LinearScaling ( zoom , - min * zoom ) ; } | Make a linear scaling from a given minimum and maximum . The minimum will be mapped to zero the maximum to one . |
33,567 | public double fMeasure ( double beta ) { final double beta2 = beta * beta ; double fmeasure = ( ( 1 + beta2 ) * pairconfuse [ 0 ] ) / ( ( 1 + beta2 ) * pairconfuse [ 0 ] + beta2 * pairconfuse [ 1 ] + pairconfuse [ 2 ] ) ; return fmeasure ; } | Get the pair - counting F - Measure |
33,568 | public void write ( TextWriterStream out , String label , Object object ) { StringBuilder buf = new StringBuilder ( 100 ) ; if ( label != null ) { buf . append ( label ) . append ( '=' ) ; } if ( object != null ) { buf . append ( object . toString ( ) ) ; } out . commentPrintLn ( buf ) ; } | Put an object into the comment section |
33,569 | protected static < I > double [ ] [ ] computeSquaredDistanceMatrix ( final List < I > col , PrimitiveDistanceFunction < ? super I > dist ) { final int size = col . size ( ) ; double [ ] [ ] imat = new double [ size ] [ size ] ; boolean squared = dist . isSquared ( ) ; FiniteProgress dprog = LOG . isVerbose ( ) ? new FiniteProgress ( "Computing distance matrix" , ( size * ( size - 1 ) ) >>> 1 , LOG ) : null ; for ( int x = 0 ; x < size ; x ++ ) { final I ox = col . get ( x ) ; for ( int y = x + 1 ; y < size ; y ++ ) { final I oy = col . get ( y ) ; double distance = dist . distance ( ox , oy ) ; distance *= squared ? - .5 : ( - .5 * distance ) ; imat [ x ] [ y ] = imat [ y ] [ x ] = distance ; } if ( dprog != null ) { dprog . setProcessed ( dprog . getProcessed ( ) + size - x - 1 , LOG ) ; } } LOG . ensureCompleted ( dprog ) ; return imat ; } | Compute the squared distance matrix . |
33,570 | private OutlierResult getOutlierResult ( ResultHierarchy hier , Result result ) { List < OutlierResult > ors = ResultUtil . filterResults ( hier , result , OutlierResult . class ) ; if ( ! ors . isEmpty ( ) ) { return ors . get ( 0 ) ; } throw new IllegalStateException ( "Comparison algorithm expected at least one outlier result." ) ; } | Find an OutlierResult to work with . |
33,571 | protected PolynomialApproximation knnDistanceApproximation ( ) { int p_max = 0 ; double [ ] b = null ; for ( int i = 0 ; i < getNumEntries ( ) ; i ++ ) { MkAppEntry entry = getEntry ( i ) ; PolynomialApproximation approximation = entry . getKnnDistanceApproximation ( ) ; if ( b == null ) { p_max = approximation . getPolynomialOrder ( ) ; b = new double [ p_max ] ; } for ( int p = 0 ; p < p_max ; p ++ ) { b [ p ] += approximation . getB ( p ) ; } } for ( int p = 0 ; p < p_max ; p ++ ) { b [ p ] /= p_max ; } if ( LoggingConfiguration . DEBUG ) { StringBuilder msg = new StringBuilder ( ) ; msg . append ( "b " + FormatUtil . format ( b , FormatUtil . NF4 ) ) ; Logger . getLogger ( this . getClass ( ) . getName ( ) ) . fine ( msg . toString ( ) ) ; } return new PolynomialApproximation ( b ) ; } | Determines and returns the polynomial approximation for the knn distances of this node as the maximum of the polynomial approximations of all entries . |
33,572 | public void setDBIDs ( DBIDs ids ) { this . idrep . setDBIDs ( ids ) ; for ( Relation < ? > orel : this . relations ) { if ( orel instanceof ProxyView ) { ( ( ProxyView < ? > ) orel ) . setDBIDs ( this . idrep . getDBIDs ( ) ) ; } } } | Set the DBIDs to use . |
33,573 | public synchronized Collection < Progress > getProgresses ( ) { List < Progress > list = new ArrayList < > ( progresses . size ( ) ) ; Iterator < WeakReference < Progress > > iter = progresses . iterator ( ) ; while ( iter . hasNext ( ) ) { WeakReference < Progress > ref = iter . next ( ) ; if ( ref . get ( ) == null ) { iter . remove ( ) ; } else { list . add ( ref . get ( ) ) ; } } return list ; } | Get a list of progresses tracked . |
33,574 | public synchronized void addProgress ( Progress p ) { Iterator < WeakReference < Progress > > iter = progresses . iterator ( ) ; while ( iter . hasNext ( ) ) { WeakReference < Progress > ref = iter . next ( ) ; if ( ref . get ( ) == null ) { iter . remove ( ) ; } else { if ( ref . get ( ) == p ) { return ; } } } progresses . add ( new WeakReference < > ( p ) ) ; } | Add a new Progress to the tracker . |
33,575 | public synchronized IndexTreePath < DeLiCluEntry > setHandled ( DBID id , O obj ) { if ( LOG . isDebugging ( ) ) { LOG . debugFine ( "setHandled " + id + ", " + obj + "\n" ) ; } IndexTreePath < DeLiCluEntry > pathToObject = findPathToObject ( getRootPath ( ) , obj , id ) ; if ( pathToObject == null ) { throw new AbortException ( "Object not found in setHandled." ) ; } DeLiCluEntry entry = pathToObject . getEntry ( ) ; entry . setHasHandled ( true ) ; entry . setHasUnhandled ( false ) ; for ( IndexTreePath < DeLiCluEntry > path = pathToObject ; path . getParentPath ( ) != null ; path = path . getParentPath ( ) ) { DeLiCluEntry parentEntry = path . getParentPath ( ) . getEntry ( ) ; DeLiCluNode node = getNode ( parentEntry ) ; boolean hasHandled = false ; boolean hasUnhandled = false ; for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { final DeLiCluEntry nodeEntry = node . getEntry ( i ) ; hasHandled = hasHandled || nodeEntry . hasHandled ( ) ; hasUnhandled = hasUnhandled || nodeEntry . hasUnhandled ( ) ; } parentEntry . setHasUnhandled ( hasUnhandled ) ; parentEntry . setHasHandled ( hasHandled ) ; } return pathToObject ; } | Marks the specified object as handled and returns the path of node ids from the root to the objects s parent . |
33,576 | public final boolean delete ( DBIDRef id ) { O obj = relation . get ( id ) ; IndexTreePath < DeLiCluEntry > deletionPath = findPathToObject ( getRootPath ( ) , obj , id ) ; if ( deletionPath == null ) { return false ; } deletePath ( deletionPath ) ; return true ; } | Deletes the specified object from this index . |
33,577 | protected double [ ] makeSample ( int maxk ) { final Random rnd = this . rnd . getSingleThreadedRandom ( ) ; double [ ] dists = new double [ maxk + 1 ] ; final double e = 1. / dim ; for ( int i = 0 ; i <= maxk ; i ++ ) { dists [ i ] = FastMath . pow ( rnd . nextDouble ( ) , e ) ; } Arrays . sort ( dists ) ; return dists ; } | Generate a data sample . |
33,578 | public final boolean setRoutingObjectID ( DBID objectID ) { if ( objectID == routingObjectID || DBIDUtil . equal ( objectID , routingObjectID ) ) { return false ; } this . routingObjectID = objectID ; return true ; } | Sets the id of the routing object of this entry . |
33,579 | public void writeExternal ( ObjectOutput out ) throws IOException { out . writeInt ( id ) ; out . writeInt ( DBIDUtil . asInteger ( routingObjectID ) ) ; out . writeDouble ( parentDistance ) ; out . writeDouble ( coveringRadius ) ; } | Calls the super method and writes the routingObjectID the parentDistance and the coveringRadius of this entry to the specified stream . |
33,580 | public void write ( TextWriterStream out , String label , TextWriteable obj ) { obj . writeToText ( out , label ) ; } | Use the objects own text serialization . |
33,581 | protected void evaluteResult ( Database db , Clustering < ? > c , Clustering < ? > refc ) { ClusterContingencyTable contmat = new ClusterContingencyTable ( selfPairing , noiseSpecialHandling ) ; contmat . process ( refc , c ) ; ScoreResult sr = new ScoreResult ( contmat ) ; sr . addHeader ( c . getLongName ( ) ) ; db . getHierarchy ( ) . add ( c , sr ) ; } | Evaluate a clustering result . |
33,582 | private boolean isReferenceResult ( Clustering < ? > t ) { return "bylabel-clustering" . equals ( t . getShortName ( ) ) || "bymodel-clustering" . equals ( t . getShortName ( ) ) || "allinone-clustering" . equals ( t . getShortName ( ) ) || "allinnoise-clustering" . equals ( t . getShortName ( ) ) ; } | Test if a clustering result is a valid reference result . |
33,583 | public final synchronized int writePage ( P page ) { int pageid = setPageID ( page ) ; writePage ( pageid , page ) ; return pageid ; } | Writes a page into this file . The method tests if the page has already an id otherwise a new id is assigned and returned . |
33,584 | public static Element drawManhattan ( SVGPlot svgp , Projection2D proj , NumberVector mid , double radius ) { final double [ ] v_mid = mid . toArray ( ) ; final long [ ] dims = proj . getVisibleDimensions2D ( ) ; SVGPath path = new SVGPath ( ) ; for ( int dim = BitsUtil . nextSetBit ( dims , 0 ) ; dim >= 0 ; dim = BitsUtil . nextSetBit ( dims , dim + 1 ) ) { v_mid [ dim ] += radius ; double [ ] p1 = proj . fastProjectDataToRenderSpace ( v_mid ) ; v_mid [ dim ] -= radius * 2 ; double [ ] p2 = proj . fastProjectDataToRenderSpace ( v_mid ) ; v_mid [ dim ] += radius ; for ( int dim2 = BitsUtil . nextSetBit ( dims , 0 ) ; dim2 >= 0 ; dim2 = BitsUtil . nextSetBit ( dims , dim2 + 1 ) ) { if ( dim < dim2 ) { v_mid [ dim2 ] += radius ; double [ ] p3 = proj . fastProjectDataToRenderSpace ( v_mid ) ; v_mid [ dim2 ] -= radius * 2 ; double [ ] p4 = proj . fastProjectDataToRenderSpace ( v_mid ) ; v_mid [ dim2 ] += radius ; path . moveTo ( p1 [ 0 ] , p1 [ 1 ] ) . drawTo ( p3 [ 0 ] , p3 [ 1 ] ) . moveTo ( p1 [ 0 ] , p1 [ 1 ] ) . drawTo ( p4 [ 0 ] , p4 [ 1 ] ) . moveTo ( p2 [ 0 ] , p2 [ 1 ] ) . drawTo ( p3 [ 0 ] , p3 [ 1 ] ) . moveTo ( p2 [ 0 ] , p2 [ 1 ] ) . drawTo ( p4 [ 0 ] , p4 [ 1 ] ) . close ( ) ; } } } return path . makeElement ( svgp ) ; } | Wireframe manhattan hypersphere |
33,585 | public static Element drawCross ( SVGPlot svgp , Projection2D proj , NumberVector mid , double radius ) { final double [ ] v_mid = mid . toArray ( ) ; final long [ ] dims = proj . getVisibleDimensions2D ( ) ; SVGPath path = new SVGPath ( ) ; for ( int dim = BitsUtil . nextSetBit ( dims , 0 ) ; dim >= 0 ; dim = BitsUtil . nextSetBit ( dims , dim + 1 ) ) { v_mid [ dim ] += radius ; double [ ] p1 = proj . fastProjectDataToRenderSpace ( v_mid ) ; v_mid [ dim ] -= radius * 2 ; double [ ] p2 = proj . fastProjectDataToRenderSpace ( v_mid ) ; v_mid [ dim ] += radius ; path . moveTo ( p1 [ 0 ] , p1 [ 1 ] ) . drawTo ( p2 [ 0 ] , p2 [ 1 ] ) . close ( ) ; } return path . makeElement ( svgp ) ; } | Wireframe cross hypersphere |
33,586 | @ SuppressWarnings ( { "cast" , "unchecked" } ) public static < O extends SpatialComparable > RangeQuery < O > getRangeQuery ( AbstractRStarTree < ? , ? , ? > tree , SpatialDistanceQuery < O > distanceQuery , Object ... hints ) { SpatialPrimitiveDistanceFunction < ? super O > df = distanceQuery . getDistanceFunction ( ) ; if ( EuclideanDistanceFunction . STATIC . equals ( df ) ) { return ( RangeQuery < O > ) new EuclideanRStarTreeRangeQuery < > ( tree , ( Relation < NumberVector > ) distanceQuery . getRelation ( ) ) ; } return new RStarTreeRangeQuery < > ( tree , distanceQuery . getRelation ( ) , df ) ; } | Get an RTree range query using an optimized double implementation when possible . |
33,587 | @ SuppressWarnings ( { "cast" , "unchecked" } ) public static < O extends SpatialComparable > KNNQuery < O > getKNNQuery ( AbstractRStarTree < ? , ? , ? > tree , SpatialDistanceQuery < O > distanceQuery , Object ... hints ) { SpatialPrimitiveDistanceFunction < ? super O > df = distanceQuery . getDistanceFunction ( ) ; if ( EuclideanDistanceFunction . STATIC . equals ( df ) ) { return ( KNNQuery < O > ) new EuclideanRStarTreeKNNQuery < > ( tree , ( Relation < NumberVector > ) distanceQuery . getRelation ( ) ) ; } return new RStarTreeKNNQuery < > ( tree , distanceQuery . getRelation ( ) , df ) ; } | Get an RTree knn query using an optimized double implementation when possible . |
33,588 | private void privateReconfigureLogging ( String pkg , final String name ) { LogManager logManager = LogManager . getLogManager ( ) ; Logger logger = Logger . getLogger ( LoggingConfiguration . class . getName ( ) ) ; if ( pkg == null ) { pkg = "" ; } String cfgfile = name ; if ( new File ( name ) . exists ( ) ) { cfgfile = name ; } else { cfgfile = pkg . replace ( '.' , File . separatorChar ) + File . separatorChar + name ; } try { InputStream cfgdata = openSystemFile ( cfgfile ) ; logManager . readConfiguration ( cfgdata ) ; InputStream cfgdata2 = openSystemFile ( cfgfile ) ; Properties cfgprop = new Properties ( ) ; cfgprop . load ( cfgdata2 ) ; DEBUG = Boolean . parseBoolean ( cfgprop . getProperty ( "debug" ) ) ; logger . info ( "Logging configuration read." ) ; } catch ( FileNotFoundException e ) { logger . log ( Level . SEVERE , "Could not find logging configuration file: " + cfgfile , e ) ; } catch ( Exception e ) { logger . log ( Level . SEVERE , "Failed to configure logging from file: " + cfgfile , e ) ; } } | Reconfigure logging . |
33,589 | private static InputStream openSystemFile ( String filename ) throws FileNotFoundException { try { return new FileInputStream ( filename ) ; } catch ( FileNotFoundException e ) { String resname = File . separatorChar != '/' ? filename . replace ( File . separatorChar , '/' ) : filename ; ClassLoader cl = LoggingConfiguration . class . getClassLoader ( ) ; InputStream result = cl . getResourceAsStream ( resname ) ; if ( result != null ) { return result ; } URL u = cl . getResource ( resname ) ; if ( u == null ) { throw e ; } try { URLConnection conn = u . openConnection ( ) ; conn . setUseCaches ( false ) ; result = conn . getInputStream ( ) ; if ( result != null ) { return result ; } } catch ( IOException x ) { throw e ; } throw e ; } } | Private copy from FileUtil to avoid cross - dependencies . Try to open a file first trying the file system then falling back to the classpath . |
33,590 | public static void setVerbose ( java . util . logging . Level verbose ) { if ( verbose . intValue ( ) <= Level . VERBOSE . intValue ( ) ) { if ( LOGGER_GLOBAL_TOP . getLevel ( ) == null || LOGGER_GLOBAL_TOP . getLevel ( ) . intValue ( ) > verbose . intValue ( ) ) { LOGGER_GLOBAL_TOP . setLevel ( verbose ) ; } if ( LOGGER_ELKI_TOP . getLevel ( ) == null || LOGGER_ELKI_TOP . getLevel ( ) . intValue ( ) > verbose . intValue ( ) ) { LOGGER_ELKI_TOP . setLevel ( verbose ) ; } } else { if ( LOGGER_GLOBAL_TOP . getLevel ( ) != null && ( Level . VERBOSE . equals ( LOGGER_GLOBAL_TOP . getLevel ( ) ) || Level . VERYVERBOSE . equals ( LOGGER_GLOBAL_TOP . getLevel ( ) ) ) ) { LOGGER_GLOBAL_TOP . setLevel ( verbose ) ; } if ( LOGGER_ELKI_TOP . getLevel ( ) != null && ( Level . VERBOSE . equals ( LOGGER_ELKI_TOP . getLevel ( ) ) || Level . VERYVERBOSE . equals ( LOGGER_ELKI_TOP . getLevel ( ) ) ) ) { LOGGER_ELKI_TOP . setLevel ( verbose ) ; } } } | Reconfigure logging to enable verbose logging at the top level . |
33,591 | public static void setStatistics ( ) { if ( LOGGER_GLOBAL_TOP . getLevel ( ) == null || LOGGER_GLOBAL_TOP . getLevel ( ) . intValue ( ) > Level . STATISTICS . intValue ( ) ) { LOGGER_GLOBAL_TOP . setLevel ( Level . STATISTICS ) ; } if ( LOGGER_ELKI_TOP . getLevel ( ) == null || LOGGER_ELKI_TOP . getLevel ( ) . intValue ( ) > Level . STATISTICS . intValue ( ) ) { LOGGER_ELKI_TOP . setLevel ( Level . STATISTICS ) ; } if ( LOGGER_TIME_TOP . getLevel ( ) == null || LOGGER_TIME_TOP . getLevel ( ) . intValue ( ) > Level . STATISTICS . intValue ( ) ) { LOGGER_TIME_TOP . setLevel ( Level . STATISTICS ) ; } } | Enable runtime performance logging . |
33,592 | public static void replaceDefaultHandler ( Handler handler ) { Logger rootlogger = LogManager . getLogManager ( ) . getLogger ( "" ) ; for ( Handler h : rootlogger . getHandlers ( ) ) { if ( h instanceof CLISmartHandler ) { rootlogger . removeHandler ( h ) ; } } addHandler ( handler ) ; } | Replace the default log handler with the given log handler . |
33,593 | public static void setDefaultLevel ( java . util . logging . Level level ) { Logger . getLogger ( TOPLEVEL_PACKAGE ) . setLevel ( level ) ; } | Set the default level . |
33,594 | public static double [ ] getRelativeCoordinates ( Event evt , Element reference ) { if ( evt instanceof DOMMouseEvent && reference instanceof SVGLocatable && reference instanceof SVGElement ) { DOMMouseEvent gnme = ( DOMMouseEvent ) evt ; SVGMatrix mat = ( ( SVGLocatable ) reference ) . getScreenCTM ( ) ; SVGMatrix imat = mat . inverse ( ) ; SVGPoint cPt = ( ( SVGElement ) reference ) . getOwnerSVGElement ( ) . createSVGPoint ( ) ; cPt . setX ( gnme . getClientX ( ) ) ; cPt . setY ( gnme . getClientY ( ) ) ; cPt = cPt . matrixTransform ( imat ) ; return new double [ ] { cPt . getX ( ) , cPt . getY ( ) } ; } return null ; } | Get the relative coordinates of a point within the coordinate system of a particular SVG Element . |
33,595 | public Clustering < MeanModel > run ( Relation < NumberVector > relation ) { final int dim = RelationUtil . dimensionality ( relation ) ; CFTree tree = cffactory . newTree ( relation . getDBIDs ( ) , relation ) ; Map < ClusteringFeature , ModifiableDBIDs > idmap = new HashMap < ClusteringFeature , ModifiableDBIDs > ( tree . leaves ) ; for ( DBIDIter iter = relation . iterDBIDs ( ) ; iter . valid ( ) ; iter . advance ( ) ) { ClusteringFeature cf = tree . findLeaf ( relation . get ( iter ) ) ; ModifiableDBIDs ids = idmap . get ( cf ) ; if ( ids == null ) { idmap . put ( cf , ids = DBIDUtil . newArray ( cf . n ) ) ; } ids . add ( iter ) ; } Clustering < MeanModel > result = new Clustering < > ( "BIRCH-leaves" , "BIRCH leaves" ) ; for ( Map . Entry < ClusteringFeature , ModifiableDBIDs > ent : idmap . entrySet ( ) ) { ClusteringFeature leaf = ent . getKey ( ) ; double [ ] center = new double [ dim ] ; for ( int i = 0 ; i < dim ; i ++ ) { center [ i ] = leaf . centroid ( i ) ; } result . addToplevelCluster ( new Cluster < > ( ent . getValue ( ) , new MeanModel ( center ) ) ) ; } return result ; } | Run the clustering algorithm . |
33,596 | public OutlierResult run ( Database database , Relation < O > relation ) { KNNQuery < O > knnq = QueryUtil . getKNNQuery ( relation , getDistanceFunction ( ) , k ) ; WritableDoubleDataStore scores = DataStoreUtil . makeDoubleStorage ( relation . getDBIDs ( ) , DataStoreFactory . HINT_DB ) ; DoubleMinMax minmax = new DoubleMinMax ( ) ; for ( DBIDIter iter = relation . iterDBIDs ( ) ; iter . valid ( ) ; iter . advance ( ) ) { KNNList neighbors = knnq . getKNNForDBID ( iter , k ) ; MeanVariance mv = new MeanVariance ( ) ; for ( DoubleDBIDListIter neighbor = neighbors . iter ( ) ; neighbor . valid ( ) ; neighbor . advance ( ) ) { if ( DBIDUtil . equal ( iter , neighbor ) ) { continue ; } mv . put ( neighbor . doubleValue ( ) ) ; } scores . putDouble ( iter , mv . getSampleStddev ( ) ) ; } OutlierScoreMeta meta = new BasicOutlierScoreMeta ( minmax . getMin ( ) , minmax . getMax ( ) , 0 , Double . POSITIVE_INFINITY ) ; DoubleRelation rel = new MaterializedDoubleRelation ( relation . getDBIDs ( ) , "stddev-outlier" , scores ) ; return new OutlierResult ( meta , rel ) ; } | Run the outlier detection algorithm |
33,597 | public static void main ( String [ ] args ) { if ( args . length > 0 && args [ 0 ] . charAt ( 0 ) != '-' ) { Class < ? > cls = ELKIServiceRegistry . findImplementation ( AbstractApplication . class , args [ 0 ] ) ; if ( cls != null ) { try { Method m = cls . getMethod ( "main" , String [ ] . class ) ; Object a = Arrays . copyOfRange ( args , 1 , args . length ) ; m . invoke ( null , a ) ; } catch ( InvocationTargetException e ) { LoggingUtil . exception ( e . getCause ( ) ) ; } catch ( Exception e ) { LoggingUtil . exception ( e ) ; } return ; } } try { Method m = DEFAULT_APPLICATION . getMethod ( "main" , String [ ] . class ) ; m . invoke ( null , ( Object ) args ) ; } catch ( Exception e ) { LoggingUtil . exception ( e ) ; } } | Launch ELKI . |
33,598 | public static Parameterizer getParameterizer ( Class < ? > c ) { for ( Class < ? > inner : c . getDeclaredClasses ( ) ) { if ( Parameterizer . class . isAssignableFrom ( inner ) ) { try { return inner . asSubclass ( Parameterizer . class ) . newInstance ( ) ; } catch ( Exception e ) { LOG . warning ( "Non-usable Parameterizer in class: " + c . getName ( ) ) ; } } } return null ; } | Get a parameterizer for the given class . |
33,599 | private void putRec ( O obj , Rec < O > rec ) { graph . put ( obj , rec ) ; for ( int i = 0 ; i < numelems ; ++ i ) { if ( obj == elems [ i ] ) { return ; } } if ( elems . length == numelems ) { elems = Arrays . copyOf ( elems , ( elems . length << 1 ) + 1 ) ; } elems [ numelems ++ ] = obj ; } | Put a record . |
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