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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 )...
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 .
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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 .
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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 .
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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 ....
Get the common split dimensions from a list of split histories .
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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 n...
Statically handle the URL access .
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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 ....
Update the global centroid .
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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 ( p...
Extract the prototype of a given cluster . When the argument is not a valid cluster of this Pointer Hierarchy the return value is unspecified .
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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 ( ) ...
Bulk - load the index .
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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 ....
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 .
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protected void addToStatistics ( ClusteringFeature other ) { n += other . n ; VMath . plusEquals ( ls , other . ls ) ; ss += other . ss ; }
Merge an other clustering features .
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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 .
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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 .
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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 ] = ...
Insertion sort for short arrays .
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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 ; {...
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?
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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 = ...
Compute Covariance Matrix for a QueryResult Collection .
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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 . ma...
Full instantiation interface .
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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" ) ; ...
Write an HTML document to an output stream .
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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...
Append a multiline text to a node transforming linewraps into BR tags .
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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 .
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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 ...
Runs the CBLOF algorithm on the given database .
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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 ) ...
Compute the boundary index separating the large cluster from the small cluster .
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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 > largeClusterM...
Compute the CBLOF scores for all the data .
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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 { ...
Load the XML configuration file .
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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 )...
Process a dataset Element in the XML stream .
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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 ...
Process a cluster Element in the XML stream .
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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 ) ....
Process a uniform Element in the XML stream .
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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 = ( ( Eleme...
Process a normal Element in the XML stream .
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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 ) . getAttribut...
Process a gamma Element in the XML stream .
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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 = ( ( E...
Process a rotate Element in the XML stream .
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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 trans...
Process a translate Element in the XML stream .
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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 ) . getAttri...
Process a clipping Element in the XML stream .
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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...
Process a static cluster Element in the XML stream .
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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 AbortExcept...
Parse a string into a vector .
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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 ( ) ) ...
Get the representative vector for a cluster model .
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private long [ ] determinePreferenceVectorByApriori ( Relation < V > relation , ModifiableDBIDs [ ] neighborIDs , StringBuilder msg ) { int dimensionality = neighborIDs . length ; UpdatableDatabase apriori_db = new HashmapDatabase ( ) ; SimpleTypeInformation < ? > bitmeta = VectorFieldTypeInformation . typeRequest ( Bi...
Determines the preference vector with the apriori strategy .
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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...
Determines the preference vector with the max intersection strategy .
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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 .
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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 (...
Returns the index of the set having the maximum intersection set with the specified set contained in the specified map .
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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 . getRangeQue...
Initializes the dimension selecting distancefunctions to determine the preference vectors .
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@ 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 , lon...
Compute the Tau correlation measure
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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 ) ...
Retrieve all properties and check them .
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@ 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 == n...
Check if aliases are listed completely .
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public LinearEquationSystem getNormalizedLinearEquationSystem ( Normalization < ? > normalization ) throws NonNumericFeaturesException { if ( normalization != null ) { LinearEquationSystem lq = normalization . transform ( linearEquationSystem ) ; lq . solveByTotalPivotSearch ( ) ; return lq ; } else { return linearEqua...
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 .
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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 .
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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 .
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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 .
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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 = getNormalizedLinearEquati...
Text output of the equation system
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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 ( ( ( SpatialDire...
Expands the spatial nodes of the specified pair .
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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 numEn...
Expands the specified directory nodes .
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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 ...
Expands the specified leaf nodes .
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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 < De...
Reinserts the objects of the already expanded nodes .
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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 .
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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 .
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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 .
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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 .
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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 . getMi...
Returns true if this SpatialComparable contains the given point false otherwise .
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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 ...
Compute the enlargement obtained by adding an object to an existing object .
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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 .
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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 . getMi...
Computes the volume of the overlapping box between two SpatialComparables .
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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 ...
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 .
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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 unio...
Returns the union of the two specified MBRs . Tolerant of null values .
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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 .
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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 .
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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
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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
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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 Fi...
Compute the squared distance matrix .
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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 outl...
Find an OutlierResult to work with .
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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 . getPo...
Determines and returns the polynomial approximation for the knn distances of this node as the maximum of the polynomial approximations of all entries .
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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 .
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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 )...
Get a list of progresses tracked .
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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 ; } } } progre...
Add a new Progress to the tracker .
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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 AbortE...
Marks the specified object as handled and returns the path of node ids from the root to the objects s parent .
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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 .
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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 dist...
Generate a data sample .
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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 .
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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 .
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public void write ( TextWriterStream out , String label , TextWriteable obj ) { obj . writeToText ( out , label ) ; }
Use the objects own text serialization .
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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 ....
Evaluate a clustering result .
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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 .
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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 .
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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 = Bits...
Wireframe manhattan hypersphere
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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...
Wireframe cross hypersphere
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@ SuppressWarnings ( { "cast" , "unchecked" } ) public static < O extends SpatialComparable > RangeQuery < O > getRangeQuery ( AbstractRStarTree < ? , ? , ? > tree , SpatialDistanceQuery < O > distanceQuery , Object ... hints ) { SpatialPrimitiveDistanceFunction < ? super O > df = distanceQuery . getDistanceFunction ( ...
Get an RTree range query using an optimized double implementation when possible .
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@ SuppressWarnings ( { "cast" , "unchecked" } ) public static < O extends SpatialComparable > KNNQuery < O > getKNNQuery ( AbstractRStarTree < ? , ? , ? > tree , SpatialDistanceQuery < O > distanceQuery , Object ... hints ) { SpatialPrimitiveDistanceFunction < ? super O > df = distanceQuery . getDistanceFunction ( ) ; ...
Get an RTree knn query using an optimized double implementation when possible .
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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 ( ) ) { cfgfi...
Reconfigure logging .
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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 = LoggingConfigu...
Private copy from FileUtil to avoid cross - dependencies . Try to open a file first trying the file system then falling back to the classpath .
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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 ( LOGG...
Reconfigure logging to enable verbose logging at the top level .
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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...
Enable runtime performance logging .
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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 .
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public static void setDefaultLevel ( java . util . logging . Level level ) { Logger . getLogger ( TOPLEVEL_PACKAGE ) . setLevel ( level ) ; }
Set the default level .
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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 ima...
Get the relative coordinates of a point within the coordinate system of a particular SVG Element .
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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 > ( t...
Run the clustering algorithm .
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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 Do...
Run the outlier detection algorithm
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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...
Launch ELKI .
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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 Parame...
Get a parameterizer for the given class .
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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 .