idx int64 0 41.2k | question stringlengths 83 4.15k | target stringlengths 5 715 |
|---|---|---|
34,300 | public static List < CollectionResult < ? > > getCollectionResults ( Result r ) { if ( r instanceof CollectionResult < ? > ) { List < CollectionResult < ? > > crs = new ArrayList < > ( 1 ) ; crs . add ( ( CollectionResult < ? > ) r ) ; return crs ; } if ( r instanceof HierarchicalResult ) { return filterResults ( ( ( H... | Collect all collection results from a Result |
34,301 | public static List < IterableResult < ? > > getIterableResults ( Result r ) { if ( r instanceof IterableResult < ? > ) { List < IterableResult < ? > > irs = new ArrayList < > ( 1 ) ; irs . add ( ( IterableResult < ? > ) r ) ; return irs ; } if ( r instanceof HierarchicalResult ) { return filterResults ( ( ( Hierarchica... | Return all Iterable results |
34,302 | public static < C extends Result > ArrayList < C > filterResults ( ResultHierarchy hier , Result r , Class < ? super C > restrictionClass ) { ArrayList < C > res = new ArrayList < > ( ) ; final It < C > it = hier . iterDescendantsSelf ( r ) . filter ( restrictionClass ) ; it . forEach ( res :: add ) ; return res ; } | Return only results of the given restriction class |
34,303 | public static void addChildResult ( HierarchicalResult parent , Result child ) { parent . getHierarchy ( ) . add ( parent , child ) ; } | Add a child result . |
34,304 | public static Database findDatabase ( ResultHierarchy hier , Result baseResult ) { final List < Database > dbs = filterResults ( hier , baseResult , Database . class ) ; return ( ! dbs . isEmpty ( ) ) ? dbs . get ( 0 ) : null ; } | Find the first database result in the tree . |
34,305 | public static void removeRecursive ( ResultHierarchy hierarchy , Result child ) { for ( It < Result > iter = hierarchy . iterParents ( child ) ; iter . valid ( ) ; iter . advance ( ) ) { hierarchy . remove ( iter . get ( ) , child ) ; } for ( It < Result > iter = hierarchy . iterChildren ( child ) ; iter . valid ( ) ; ... | Recursively remove a result and its children . |
34,306 | protected void findEigenVectors ( double [ ] [ ] imat , double [ ] [ ] evs , double [ ] lambda ) { final int size = imat . length ; Random rnd = random . getSingleThreadedRandom ( ) ; double [ ] tmp = new double [ size ] ; FiniteProgress prog = LOG . isVerbose ( ) ? new FiniteProgress ( "Learning projections" , tdim , ... | Find the first eigenvectors and eigenvalues using power iterations . |
34,307 | protected void randomInitialization ( double [ ] out , Random rnd ) { double l2 = 0. ; while ( ! ( l2 > 0 ) ) { for ( int d = 0 ; d < out . length ; d ++ ) { final double val = rnd . nextDouble ( ) ; out [ d ] = val ; l2 += val * val ; } } final double s = 1. / FastMath . sqrt ( l2 ) ; for ( int d = 0 ; d < out . lengt... | Choose a random vector of unit norm for power iterations . |
34,308 | protected double updateEigenvector ( double [ ] in , double [ ] out , double l ) { double s = 1. / ( l > 0. ? l : l < 0. ? - l : 1. ) ; s = ( in [ 0 ] > 0. ) ? s : - s ; double diff = 0. ; for ( int d = 0 ; d < in . length ; d ++ ) { in [ d ] *= s ; double delta = in [ d ] - out [ d ] ; diff += delta * delta ; out [ d ... | Compute the change in the eigenvector and normalize the output vector while doing so . |
34,309 | protected void updateMatrix ( double [ ] [ ] mat , final double [ ] evec , double eval ) { final int size = mat . length ; for ( int i = 0 ; i < size ; i ++ ) { final double [ ] mati = mat [ i ] ; final double eveci = evec [ i ] ; for ( int j = 0 ; j < size ; j ++ ) { mati [ j ] -= eval * eveci * evec [ j ] ; } } } | Update matrix by removing the effects of a known Eigenvector . |
34,310 | public static double pdf ( double x , double mu , double sigma , double k ) { if ( x == Double . POSITIVE_INFINITY || x == Double . NEGATIVE_INFINITY ) { return 0. ; } x = ( x - mu ) / sigma ; if ( k > 0 || k < 0 ) { if ( k * x > 1 ) { return 0. ; } double t = FastMath . log ( 1 - k * x ) ; return t == Double . NEGATIV... | PDF of GEV distribution |
34,311 | public static double cdf ( double val , double mu , double sigma , double k ) { final double x = ( val - mu ) / sigma ; if ( k > 0 || k < 0 ) { if ( k * x > 1 ) { return k > 0 ? 1 : 0 ; } return FastMath . exp ( - FastMath . exp ( FastMath . log ( 1 - k * x ) / k ) ) ; } else { return FastMath . exp ( - FastMath . exp ... | CDF of GEV distribution |
34,312 | public static double quantile ( double val , double mu , double sigma , double k ) { if ( val < 0.0 || val > 1.0 ) { return Double . NaN ; } if ( k < 0 ) { return mu + sigma * Math . max ( ( 1. - FastMath . pow ( - FastMath . log ( val ) , k ) ) / k , 1. / k ) ; } else if ( k > 0 ) { return mu + sigma * Math . min ( ( ... | Quantile function of GEV distribution |
34,313 | public static double cdf ( double x , double sigma ) { if ( x <= 0. ) { return 0. ; } final double xs = x / sigma ; return 1. - FastMath . exp ( - .5 * xs * xs ) ; } | CDF of Rayleigh distribution |
34,314 | public static double quantile ( double val , double sigma ) { if ( ! ( val >= 0. ) || ! ( val <= 1. ) ) { return Double . NaN ; } if ( val == 0. ) { return 0. ; } if ( val == 1. ) { return Double . POSITIVE_INFINITY ; } return sigma * FastMath . sqrt ( - 2. * FastMath . log ( 1. - val ) ) ; } | Quantile function of Rayleigh distribution |
34,315 | public OutlierResult run ( Database db , Relation < V > relation ) { ArrayDBIDs ids = DBIDUtil . ensureArray ( relation . getDBIDs ( ) ) ; SimilarityQuery < V > sq = db . getSimilarityQuery ( relation , kernelFunction ) ; KernelMatrix kernelMatrix = new KernelMatrix ( sq , relation , ids ) ; WritableDoubleDataStore abo... | Run ABOD on the data set . |
34,316 | protected double computeABOF ( KernelMatrix kernelMatrix , DBIDRef pA , DBIDArrayIter pB , DBIDArrayIter pC , MeanVariance s ) { s . reset ( ) ; double simAA = kernelMatrix . getSimilarity ( pA , pA ) ; for ( pB . seek ( 0 ) ; pB . valid ( ) ; pB . advance ( ) ) { if ( DBIDUtil . equal ( pB , pA ) ) { continue ; } doub... | Compute the exact ABOF value . |
34,317 | public OutlierResult run ( Database database , Relation < O > relation ) { DBIDs ids = relation . getDBIDs ( ) ; WritableDoubleDataStore store = DataStoreUtil . makeDoubleStorage ( ids , DataStoreFactory . HINT_DB ) ; DistanceQuery < O > distq = database . getDistanceQuery ( relation , getDistanceFunction ( ) ) ; KNNQu... | Run the parallel kNN weight outlier detector . |
34,318 | public Clustering < ? > run ( final Database database , final Relation < DiscreteUncertainObject > relation ) { if ( relation . size ( ) <= 0 ) { return new Clustering < > ( "Uk-Means Clustering" , "ukmeans-clustering" ) ; } DBIDs sampleids = DBIDUtil . randomSample ( relation . getDBIDs ( ) , k , rnd ) ; List < double... | Run the clustering . |
34,319 | protected boolean updateAssignment ( DBIDIter iditer , List < ? extends ModifiableDBIDs > clusters , WritableIntegerDataStore assignment , int newA ) { final int oldA = assignment . intValue ( iditer ) ; if ( oldA == newA ) { return false ; } clusters . get ( newA ) . add ( iditer ) ; assignment . putInt ( iditer , new... | Update the cluster assignment . |
34,320 | protected double getExpectedRepDistance ( NumberVector rep , DiscreteUncertainObject uo ) { SquaredEuclideanDistanceFunction euclidean = SquaredEuclideanDistanceFunction . STATIC ; int counter = 0 ; double sum = 0.0 ; for ( int i = 0 ; i < uo . getNumberSamples ( ) ; i ++ ) { sum += euclidean . distance ( rep , uo . ge... | Get expected distance between a Vector and an uncertain object |
34,321 | protected void logVarstat ( DoubleStatistic varstat , double [ ] varsum ) { if ( varstat != null ) { double s = sum ( varsum ) ; getLogger ( ) . statistics ( varstat . setDouble ( s ) ) ; } } | Log statistics on the variance sum . |
34,322 | public void save ( ) throws FileNotFoundException { PrintStream p = new PrintStream ( file ) ; p . println ( COMMENT_PREFIX + "Saved ELKI settings. First line is title, remaining lines are parameters." ) ; for ( Pair < String , ArrayList < String > > settings : store ) { p . println ( settings . first ) ; for ( String ... | Save the current data to the given file . |
34,323 | public void load ( ) throws FileNotFoundException , IOException { BufferedReader is = new BufferedReader ( new InputStreamReader ( new FileInputStream ( file ) ) ) ; ArrayList < String > buf = new ArrayList < > ( ) ; while ( is . ready ( ) ) { String line = is . readLine ( ) ; if ( line . startsWith ( COMMENT_PREFIX ) ... | Read the current file |
34,324 | public void remove ( String key ) { Iterator < Pair < String , ArrayList < String > > > it = store . iterator ( ) ; while ( it . hasNext ( ) ) { String thisKey = it . next ( ) . first ; if ( key . equals ( thisKey ) ) { it . remove ( ) ; break ; } } } | Remove a given key from the file . |
34,325 | public ArrayList < String > get ( String key ) { Iterator < Pair < String , ArrayList < String > > > it = store . iterator ( ) ; while ( it . hasNext ( ) ) { Pair < String , ArrayList < String > > pair = it . next ( ) ; if ( key . equals ( pair . first ) ) { return pair . second ; } } return null ; } | Find a saved setting by key . |
34,326 | public Clustering < Model > run ( Database database , Relation < V > relation ) { int dim_c = RelationUtil . dimensionality ( relation ) ; if ( dim_c < l ) { throw new IllegalStateException ( "Dimensionality of data < parameter l! " + "(" + dim_c + " < " + l + ")" ) ; } int k_c = Math . min ( relation . size ( ) , k_i ... | Performs the ORCLUS algorithm on the given database . |
34,327 | private List < ORCLUSCluster > initialSeeds ( Relation < V > database , int k ) { DBIDs randomSample = DBIDUtil . randomSample ( database . getDBIDs ( ) , k , rnd ) ; List < ORCLUSCluster > seeds = new ArrayList < > ( k ) ; for ( DBIDIter iter = randomSample . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { seeds ... | Initializes the list of seeds wit a random sample of size k . |
34,328 | private void assign ( Relation < V > database , List < ORCLUSCluster > clusters ) { NumberVectorDistanceFunction < ? super V > distFunc = SquaredEuclideanDistanceFunction . STATIC ; for ( ORCLUSCluster cluster : clusters ) { cluster . objectIDs . clear ( ) ; } List < NumberVector > projectedCentroids = new ArrayList < ... | Creates a partitioning of the database by assigning each object to its closest seed . |
34,329 | private void merge ( Relation < V > relation , List < ORCLUSCluster > clusters , int k_new , int d_new , IndefiniteProgress cprogress ) { ArrayList < ProjectedEnergy > projectedEnergies = new ArrayList < > ( ( clusters . size ( ) * ( clusters . size ( ) - 1 ) ) >>> 1 ) ; for ( int i = 0 ; i < clusters . size ( ) ; i ++... | Reduces the number of seeds to k_new |
34,330 | private ProjectedEnergy projectedEnergy ( Relation < V > relation , ORCLUSCluster c_i , ORCLUSCluster c_j , int i , int j , int dim ) { NumberVectorDistanceFunction < ? super V > distFunc = SquaredEuclideanDistanceFunction . STATIC ; ORCLUSCluster c_ij = union ( relation , c_i , c_j , dim ) ; double sum = 0. ; NumberVe... | Computes the projected energy of the specified clusters . The projected energy is given by the mean square distance of the points to the centroid of the union cluster c when all points in c are projected to the subspace of c . |
34,331 | private ORCLUSCluster union ( Relation < V > relation , ORCLUSCluster c1 , ORCLUSCluster c2 , int dim ) { ORCLUSCluster c = new ORCLUSCluster ( ) ; c . objectIDs = DBIDUtil . newHashSet ( c1 . objectIDs ) ; c . objectIDs . addDBIDs ( c2 . objectIDs ) ; c . objectIDs = DBIDUtil . newArray ( c . objectIDs ) ; if ( c . ob... | Returns the union of the two specified clusters . |
34,332 | private static void initializeNNCache ( double [ ] scratch , double [ ] bestd , int [ ] besti ) { final int size = bestd . length ; Arrays . fill ( bestd , Double . POSITIVE_INFINITY ) ; Arrays . fill ( besti , - 1 ) ; for ( int x = 0 , p = 0 ; x < size ; x ++ ) { assert ( p == MatrixParadigm . triangleSize ( x ) ) ; d... | Initialize the NN cache . |
34,333 | protected int findMerge ( int size , MatrixParadigm mat , double [ ] bestd , int [ ] besti , PointerHierarchyRepresentationBuilder builder ) { double mindist = Double . POSITIVE_INFINITY ; int x = - 1 , y = - 1 ; for ( int cx = 0 ; cx < size ; cx ++ ) { final int cy = besti [ cx ] ; if ( cy < 0 ) { continue ; } final d... | Perform the next merge step . |
34,334 | protected void merge ( int size , MatrixParadigm mat , double [ ] bestd , int [ ] besti , PointerHierarchyRepresentationBuilder builder , double mindist , int x , int y ) { final DBIDArrayIter ix = mat . ix . seek ( x ) , iy = mat . iy . seek ( y ) ; if ( LOG . isDebuggingFine ( ) ) { LOG . debugFine ( "Merging: " + DB... | Execute the cluster merge . |
34,335 | private void updateCache ( int size , double [ ] scratch , double [ ] bestd , int [ ] besti , int x , int y , int j , double d ) { if ( d <= bestd [ j ] ) { bestd [ j ] = d ; besti [ j ] = y ; return ; } if ( besti [ j ] == x || besti [ j ] == y ) { findBest ( size , scratch , bestd , besti , j ) ; } } | Update the cache . |
34,336 | public VisualizerContext newContext ( ResultHierarchy hier , Result start ) { Collection < Relation < ? > > rels = ResultUtil . filterResults ( hier , Relation . class ) ; for ( Relation < ? > rel : rels ) { if ( samplesize == 0 ) { continue ; } if ( ! ResultUtil . filterResults ( hier , rel , SamplingResult . class ) ... | Make a new visualization context |
34,337 | public static String getTitle ( Database db , Result result ) { List < TrackedParameter > settings = new ArrayList < > ( ) ; for ( SettingsResult sr : SettingsResult . getSettingsResults ( result ) ) { settings . addAll ( sr . getSettings ( ) ) ; } String algorithm = null ; String distance = null ; String dataset = nul... | Try to automatically generate a title for this . |
34,338 | protected static String shortenClassname ( String nam , char c ) { final int lastdot = nam . lastIndexOf ( c ) ; if ( lastdot >= 0 ) { nam = nam . substring ( lastdot + 1 ) ; } return nam ; } | Shorten the class name . |
34,339 | private static Class < ? > getRestrictionClass ( OptionID oid , final Parameter < ? > firstopt , Map < OptionID , List < Pair < Parameter < ? > , Class < ? > > > > byopt ) { Class < ? > superclass = getRestrictionClass ( firstopt ) ; for ( Pair < Parameter < ? > , Class < ? > > clinst : byopt . get ( oid ) ) { if ( cli... | Get the restriction class of an option . |
34,340 | private static < T > ArrayList < T > sorted ( Collection < T > cls , Comparator < ? super T > c ) { ArrayList < T > sorted = new ArrayList < > ( cls ) ; sorted . sort ( c ) ; return sorted ; } | Sort a collection of classes . |
34,341 | protected void handleHoverEvent ( Event evt ) { if ( evt . getTarget ( ) instanceof Element ) { Element e = ( Element ) evt . getTarget ( ) ; Node next = e . getNextSibling ( ) ; if ( next instanceof Element ) { toggleTooltip ( ( Element ) next , evt . getType ( ) ) ; } else { LoggingUtil . warning ( "Tooltip sibling n... | Handle the hover events . |
34,342 | protected void toggleTooltip ( Element elem , String type ) { String csscls = elem . getAttribute ( SVGConstants . SVG_CLASS_ATTRIBUTE ) ; if ( SVGConstants . SVG_MOUSEOVER_EVENT_TYPE . equals ( type ) ) { if ( TOOLTIP_HIDDEN . equals ( csscls ) ) { SVGUtil . setAtt ( elem , SVGConstants . SVG_CLASS_ATTRIBUTE , TOOLTIP... | Toggle the Tooltip of an element . |
34,343 | public DoubleDBIDList reverseKNNQuery ( DBIDRef id , int k ) { ModifiableDoubleDBIDList result = DBIDUtil . newDistanceDBIDList ( ) ; final Heap < MTreeSearchCandidate > pq = new UpdatableHeap < > ( ) ; pq . add ( new MTreeSearchCandidate ( 0. , getRootID ( ) , null , Double . NaN ) ) ; while ( ! pq . isEmpty ( ) ) { M... | Performs a reverse k - nearest neighbor query for the given object ID . The query result is in ascending order to the distance to the query object . |
34,344 | private void leafEntryIDs ( MkAppTreeNode < O > node , ModifiableDBIDs result ) { if ( node . isLeaf ( ) ) { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { MkAppEntry entry = node . getEntry ( i ) ; result . add ( ( ( LeafEntry ) entry ) . getDBID ( ) ) ; } } else { for ( int i = 0 ; i < node . getNumEntries... | Determines the ids of the leaf entries stored in the specified subtree . |
34,345 | private PolynomialApproximation approximateKnnDistances ( double [ ] knnDistances ) { StringBuilder msg = new StringBuilder ( ) ; int k_0 = 0 ; if ( settings . log ) { for ( int i = 0 ; i < settings . kmax ; i ++ ) { double dist = knnDistances [ i ] ; if ( dist == 0 ) { k_0 ++ ; } else { break ; } } } double [ ] x = ne... | Computes the polynomial approximation of the specified knn - distances . |
34,346 | protected final int isLeft ( double [ ] a , double [ ] b , double [ ] o ) { final double cross = getRX ( a , o ) * getRY ( b , o ) - getRY ( a , o ) * getRX ( b , o ) ; if ( cross == 0 ) { final double dista = Math . abs ( getRX ( a , o ) ) + Math . abs ( getRY ( a , o ) ) ; final double distb = Math . abs ( getRX ( b ... | Test whether a point is left of the other wrt . the origin . |
34,347 | private double mdist ( double [ ] a , double [ ] b ) { return Math . abs ( a [ 0 ] - b [ 0 ] ) + Math . abs ( a [ 1 ] - b [ 1 ] ) ; } | Manhattan distance . |
34,348 | private boolean isConvex ( double [ ] a , double [ ] b , double [ ] c ) { double area = ( b [ 0 ] - a [ 0 ] ) * factor * ( c [ 1 ] - a [ 1 ] ) - ( c [ 0 ] - a [ 0 ] ) * factor * ( b [ 1 ] - a [ 1 ] ) ; return ( - 1e-13 < area && area < 1e-13 ) ? ( mdist ( b , c ) > mdist ( a , b ) + mdist ( a , c ) ) : ( area < 0 ) ; } | Simple convexity test . |
34,349 | private void grahamScan ( ) { if ( points . size ( ) < 3 ) { return ; } Iterator < double [ ] > iter = points . iterator ( ) ; Stack < double [ ] > stack = new Stack < > ( ) ; final double [ ] first = iter . next ( ) ; stack . add ( first ) ; while ( iter . hasNext ( ) ) { double [ ] n = iter . next ( ) ; if ( mdist ( ... | The actual graham scan main loop . |
34,350 | public Polygon getHull ( ) { if ( ! ok ) { computeConvexHull ( ) ; } return new Polygon ( points , minmaxX . getMin ( ) , minmaxX . getMax ( ) , minmaxY . getMin ( ) , minmaxY . getMax ( ) ) ; } | Compute the convex hull and return the resulting polygon . |
34,351 | private static double coverRadius ( double [ ] [ ] matrix , int [ ] idx , int i ) { final int idx_i = idx [ i ] ; final double [ ] row_i = matrix [ i ] ; double m = 0 ; for ( int j = 0 ; j < row_i . length ; j ++ ) { if ( i != j && idx_i == idx [ j ] ) { final double d = row_i [ j ] ; m = d > m ? d : m ; } } return m ;... | Find the cover radius of a partition . |
34,352 | private static int [ ] mstPartition ( double [ ] [ ] matrix ) { final int n = matrix . length ; int [ ] edges = PrimsMinimumSpanningTree . processDense ( matrix ) ; double meanlength = thresholdLength ( matrix , edges ) ; int [ ] idx = new int [ n ] , best = new int [ n ] , sizes = new int [ n ] ; int bestsize = - 1 ; ... | Partition the data using the minimu spanning tree . |
34,353 | private static double thresholdLength ( double [ ] [ ] matrix , int [ ] edges ) { double [ ] lengths = new double [ edges . length >> 1 ] ; for ( int i = 0 , e = edges . length - 1 ; i < e ; i += 2 ) { lengths [ i >> 1 ] = matrix [ edges [ i ] ] [ edges [ i + 1 ] ] ; } Arrays . sort ( lengths ) ; final int pos = ( leng... | Choose the threshold length of edges to consider omittig . |
34,354 | private static double edgelength ( double [ ] [ ] matrix , int [ ] edges , int i ) { i <<= 1 ; return matrix [ edges [ i ] ] [ edges [ i + 1 ] ] ; } | Length of edge i . |
34,355 | private static void omitEdge ( int [ ] edges , int [ ] idx , int [ ] sizes , int omit ) { for ( int i = 0 ; i < idx . length ; i ++ ) { idx [ i ] = i ; } Arrays . fill ( sizes , 1 ) ; for ( int i = 0 , j = 0 , e = edges . length - 1 ; j < e ; i ++ , j += 2 ) { if ( i == omit ) { continue ; } int ea = edges [ j + 1 ] , ... | Partition the data by omitting one edge . |
34,356 | private static int follow ( int i , int [ ] partitions ) { int next = partitions [ i ] , tmp ; while ( i != next ) { tmp = next ; next = partitions [ i ] = partitions [ next ] ; i = tmp ; } return i ; } | Union - find with simple path compression . |
34,357 | private static void computeCentroid ( double [ ] centroid , Relation < ? extends NumberVector > relation , DBIDs ids ) { Arrays . fill ( centroid , 0 ) ; int dim = centroid . length ; for ( DBIDIter it = ids . iter ( ) ; it . valid ( ) ; it . advance ( ) ) { NumberVector v = relation . get ( it ) ; for ( int i = 0 ; i ... | Recompute the centroid of a set . |
34,358 | public static < O > DistanceQuery < O > getDistanceQuery ( Database database , DistanceFunction < ? super O > distanceFunction , Object ... hints ) { final Relation < O > objectQuery = database . getRelation ( distanceFunction . getInputTypeRestriction ( ) , hints ) ; return database . getDistanceQuery ( objectQuery , ... | Get a distance query for a given distance function automatically choosing a relation . |
34,359 | public static < O > SimilarityQuery < O > getSimilarityQuery ( Database database , SimilarityFunction < ? super O > similarityFunction , Object ... hints ) { final Relation < O > objectQuery = database . getRelation ( similarityFunction . getInputTypeRestriction ( ) , hints ) ; return database . getSimilarityQuery ( ob... | Get a similarity query automatically choosing a relation . |
34,360 | public static < O > RKNNQuery < O > getRKNNQuery ( Relation < O > relation , DistanceFunction < ? super O > distanceFunction , Object ... hints ) { final DistanceQuery < O > distanceQuery = relation . getDistanceQuery ( distanceFunction , hints ) ; return relation . getRKNNQuery ( distanceQuery , hints ) ; } | Get a rKNN query object for the given distance function . |
34,361 | public static < O > RangeQuery < O > getLinearScanSimilarityRangeQuery ( SimilarityQuery < O > simQuery ) { if ( simQuery instanceof PrimitiveSimilarityQuery ) { final PrimitiveSimilarityQuery < O > pdq = ( PrimitiveSimilarityQuery < O > ) simQuery ; return new LinearScanPrimitiveSimilarityRangeQuery < > ( pdq ) ; } re... | Get a linear scan query for the given similarity query . |
34,362 | protected static void register ( Class < ? > parent , String cname ) { Entry e = data . get ( parent ) ; if ( e == null ) { data . put ( parent , e = new Entry ( ) ) ; } e . addName ( cname ) ; } | Register a class with the registry . |
34,363 | protected static void register ( Class < ? > parent , Class < ? > clazz ) { Entry e = data . get ( parent ) ; if ( e == null ) { data . put ( parent , e = new Entry ( ) ) ; } final String cname = clazz . getCanonicalName ( ) ; e . addHit ( cname , clazz ) ; if ( clazz . isAnnotationPresent ( Alias . class ) ) { Alias a... | Register a class in the registry . |
34,364 | protected static void registerAlias ( Class < ? > parent , String alias , String cname ) { Entry e = data . get ( parent ) ; assert ( e != null ) ; e . addAlias ( alias , cname ) ; } | Register a class alias with the registry . |
34,365 | private static Class < ? > tryLoadClass ( String value ) { try { return CLASSLOADER . loadClass ( value ) ; } catch ( ClassNotFoundException e ) { return null ; } } | Attempt to load a class |
34,366 | public static List < Class < ? > > findAllImplementations ( Class < ? > restrictionClass ) { if ( restrictionClass == null ) { return Collections . emptyList ( ) ; } if ( ! contains ( restrictionClass ) ) { ELKIServiceLoader . load ( restrictionClass ) ; ELKIServiceScanner . load ( restrictionClass ) ; } Entry e = data... | Find all implementations of a particular interface . |
34,367 | public static List < Class < ? > > findAllImplementations ( Class < ? > c , boolean everything , boolean parameterizable ) { if ( c == null ) { return Collections . emptyList ( ) ; } if ( ! everything && parameterizable ) { return findAllImplementations ( c ) ; } List < Class < ? > > known = findAllImplementations ( c ... | Find all implementations of a given class in the classpath . |
34,368 | private static < C > Class < ? > tryAlternateNames ( Class < ? super C > restrictionClass , String value , Entry e ) { StringBuilder buf = new StringBuilder ( value . length ( ) + 100 ) ; Class < ? > clazz = tryLoadClass ( buf . append ( value ) . append ( FACTORY_POSTFIX ) . toString ( ) ) ; if ( clazz != null ) { ret... | Try loading alternative names . |
34,369 | protected Element setupCanvas ( ) { final double margin = context . getStyleLibrary ( ) . getSize ( StyleLibrary . MARGIN ) ; this . layer = setupCanvas ( svgp , this . proj , margin , getWidth ( ) , getHeight ( ) ) ; return layer ; } | Setup our canvas . |
34,370 | protected SimpleTypeInformation < ? > convertedType ( SimpleTypeInformation < ? > in , NumberVector . Factory < V > factory ) { return new VectorFieldTypeInformation < > ( factory , tdim ) ; } | Get the output type from the input type after conversion . |
34,371 | protected < O > Map < O , IntList > partition ( List < ? extends O > classcolumn ) { Map < O , IntList > classes = new HashMap < > ( ) ; Iterator < ? extends O > iter = classcolumn . iterator ( ) ; for ( int i = 0 ; iter . hasNext ( ) ; i ++ ) { O lbl = iter . next ( ) ; IntList ids = classes . get ( lbl ) ; if ( ids =... | Partition the bundle based on the class label . |
34,372 | public Curve makeCurve ( ) { Curve c = new Curve ( curves . size ( ) ) ; curves . add ( c ) ; return c ; } | Make a new curve . |
34,373 | public void publish ( String message , Level level ) { try { publish ( new LogRecord ( level , message ) ) ; } catch ( BadLocationException e ) { throw new RuntimeException ( "Error writing a log-like message." , e ) ; } } | Print a message as if it were logged without going through the full logger . |
34,374 | protected synchronized void publish ( LogRecord record ) throws BadLocationException { final Formatter fmt ; final Style style ; if ( record . getLevel ( ) . intValue ( ) >= Level . WARNING . intValue ( ) ) { fmt = errformat ; style = errStyle ; } else if ( record . getLevel ( ) . intValue ( ) <= Level . FINE . intValu... | Publish a log record to the logging pane . |
34,375 | protected void optimizeSNE ( AffinityMatrix pij , double [ ] [ ] sol ) { final int size = pij . size ( ) ; if ( size * 3L * dim > 0x7FFF_FFFAL ) { throw new AbortException ( "Memory exceeds Java array size limit." ) ; } double [ ] meta = new double [ size * 3 * dim ] ; final int dim3 = dim * 3 ; for ( int off = 2 * dim... | Perform the actual tSNE optimization . |
34,376 | protected double computeQij ( double [ ] [ ] qij , double [ ] [ ] solution ) { double qij_sum = 0 ; for ( int i = 1 ; i < qij . length ; i ++ ) { final double [ ] qij_i = qij [ i ] , vi = solution [ i ] ; for ( int j = 0 ; j < i ; j ++ ) { qij_sum += qij_i [ j ] = qij [ j ] [ i ] = MathUtil . exp ( - sqDist ( vi , solu... | Compute the qij of the solution and the sum . |
34,377 | protected void computeGradient ( AffinityMatrix pij , double [ ] [ ] qij , double qij_isum , double [ ] [ ] sol , double [ ] meta ) { final int dim3 = dim * 3 ; int size = pij . size ( ) ; for ( int i = 0 , off = 0 ; i < size ; i ++ , off += dim3 ) { final double [ ] sol_i = sol [ i ] , qij_i = qij [ i ] ; Arrays . fil... | Compute the gradients . |
34,378 | public OutlierResult run ( Database database , Relation < O > relation ) { DistanceFunction < ? super O > df = clusterer . getDistanceFunction ( ) ; DistanceQuery < O > dq = database . getDistanceQuery ( relation , df ) ; Clustering < ? > c = clusterer . run ( database , relation ) ; WritableDoubleDataStore scores = Da... | Run the outlier detection algorithm . |
34,379 | public FittingFunctionResult eval ( double x , double [ ] params ) { final int len = params . length ; assert ( len % 3 ) == 0 ; double y = 0.0 ; double [ ] gradients = new double [ len ] ; for ( int i = 2 ; i < params . length ; i += 3 ) { double stdpar = ( x - params [ i - 2 ] ) / params [ i - 1 ] ; double e = FastMa... | Compute the mixture of Gaussians at the given position |
34,380 | private void showVisualization ( VisualizerContext context , SimilarityMatrixVisualizer factory , VisualizationTask task ) { VisualizationPlot plot = new VisualizationPlot ( ) ; Visualization vis = factory . makeVisualization ( context , task , plot , 1.0 , 1.0 , null ) ; plot . getRoot ( ) . appendChild ( vis . getLay... | Show a single visualization . |
34,381 | public void put ( int [ ] data ) { final int l = data . length ; for ( int i = 0 ; i < l ; i ++ ) { put ( data [ i ] ) ; } } | Process a whole array of int values . |
34,382 | public OutlierResult run ( Database database , Relation < O > rel ) { final DBIDs ids = rel . getDBIDs ( ) ; LOG . verbose ( "Running kNN preprocessor." ) ; KNNQuery < O > knnq = DatabaseUtil . precomputedKNNQuery ( database , rel , getDistanceFunction ( ) , kmax + 1 ) ; WritableDataStore < double [ ] > densities = Dat... | Run the KDEOS outlier detection algorithm . |
34,383 | protected void estimateDensities ( Relation < O > rel , KNNQuery < O > knnq , final DBIDs ids , WritableDataStore < double [ ] > densities ) { final int dim = dimensionality ( rel ) ; final int knum = kmax + 1 - kmin ; for ( DBIDIter iter = ids . iter ( ) ; iter . valid ( ) ; iter . advance ( ) ) { densities . put ( it... | Perform the kernel density estimation step . |
34,384 | private int dimensionality ( Relation < O > rel ) { if ( idim >= 0 ) { return idim ; } @ SuppressWarnings ( "unchecked" ) final Relation < NumberVector > frel = ( Relation < NumberVector > ) rel ; int dim = RelationUtil . dimensionality ( frel ) ; if ( dim < 1 ) { throw new AbortException ( "When using KDEOS with non-v... | Ugly hack to allow using this implementation without having a well - defined dimensionality . |
34,385 | protected void computeOutlierScores ( KNNQuery < O > knnq , final DBIDs ids , WritableDataStore < double [ ] > densities , WritableDoubleDataStore kdeos , DoubleMinMax minmax ) { final int knum = kmax + 1 - kmin ; FiniteProgress prog = LOG . isVerbose ( ) ? new FiniteProgress ( "Computing KDEOS scores" , ids . size ( )... | Compute the final KDEOS scores . |
34,386 | public Clustering < Model > run ( Relation < V > rel ) { fulldatabase = preprocess ( rel ) ; processedIDs = DBIDUtil . newHashSet ( fulldatabase . size ( ) ) ; noiseDim = dimensionality ( fulldatabase ) ; FiniteProgress progress = LOG . isVerbose ( ) ? new FiniteProgress ( "CASH Clustering" , fulldatabase . size ( ) , ... | Run CASH on the relation . |
34,387 | private Relation < ParameterizationFunction > preprocess ( Relation < V > vrel ) { DBIDs ids = vrel . getDBIDs ( ) ; SimpleTypeInformation < ParameterizationFunction > type = new SimpleTypeInformation < > ( ParameterizationFunction . class ) ; WritableDataStore < ParameterizationFunction > prep = DataStoreUtil . makeSt... | Preprocess the dataset precomputing the parameterization functions . |
34,388 | private void initHeap ( ObjectHeap < CASHInterval > heap , Relation < ParameterizationFunction > relation , int dim , DBIDs ids ) { CASHIntervalSplit split = new CASHIntervalSplit ( relation , minPts ) ; double [ ] minMax = determineMinMaxDistance ( relation , dim ) ; double d_min = minMax [ 0 ] , d_max = minMax [ 1 ] ... | Initializes the heap with the root intervals . |
34,389 | private MaterializedRelation < ParameterizationFunction > buildDB ( int dim , double [ ] [ ] basis , DBIDs ids , Relation < ParameterizationFunction > relation ) { ProxyDatabase proxy = new ProxyDatabase ( ids ) ; SimpleTypeInformation < ParameterizationFunction > type = new SimpleTypeInformation < > ( Parameterization... | Builds a dim - 1 dimensional database where the objects are projected into the specified subspace . |
34,390 | private ParameterizationFunction project ( double [ ] [ ] basis , ParameterizationFunction f ) { double [ ] m = transposeTimes ( basis , f . getColumnVector ( ) ) ; return new ParameterizationFunction ( DoubleVector . wrap ( m ) ) ; } | Projects the specified parameterization function into the subspace described by the given basis . |
34,391 | private double [ ] [ ] determineBasis ( double [ ] alpha ) { final int dim = alpha . length ; double [ ] nn = new double [ dim + 1 ] ; for ( int i = 0 ; i < nn . length ; i ++ ) { double alpha_i = i == alpha . length ? 0 : alpha [ i ] ; nn [ i ] = ParameterizationFunction . sinusProduct ( 0 , i , alpha ) * FastMath . c... | Determines a basis defining a subspace described by the specified alpha values . |
34,392 | private CASHInterval determineNextIntervalAtMaxLevel ( ObjectHeap < CASHInterval > heap ) { CASHInterval next = doDetermineNextIntervalAtMaxLevel ( heap ) ; while ( next == null ) { if ( heap . isEmpty ( ) ) { return null ; } next = doDetermineNextIntervalAtMaxLevel ( heap ) ; } return next ; } | Determines the next best interval at maximum level i . e . the next interval containing the most unprocessed objects . |
34,393 | private CASHInterval doDetermineNextIntervalAtMaxLevel ( ObjectHeap < CASHInterval > heap ) { CASHInterval interval = heap . poll ( ) ; int dim = interval . getDimensionality ( ) ; while ( true ) { if ( interval . getLevel ( ) >= maxLevel && interval . getMaxSplitDimension ( ) == ( dim - 1 ) ) { return interval ; } if ... | Recursive helper method to determine the next best interval at maximum level i . e . the next interval containing the most unprocessed objects |
34,394 | private double [ ] determineMinMaxDistance ( Relation < ParameterizationFunction > relation , int dimensionality ) { double [ ] min = new double [ dimensionality - 1 ] ; double [ ] max = new double [ dimensionality - 1 ] ; Arrays . fill ( max , Math . PI ) ; HyperBoundingBox box = new HyperBoundingBox ( min , max ) ; d... | Determines the minimum and maximum function value of all parameterization functions stored in the specified database . |
34,395 | public HistogramResult run ( Database database , Relation < O > relation ) { final DistanceQuery < O > distanceQuery = database . getDistanceQuery ( relation , getDistanceFunction ( ) ) ; final KNNQuery < O > knnQuery = database . getKNNQuery ( distanceQuery , relation . size ( ) ) ; if ( LOG . isVerbose ( ) ) { LOG . ... | Process a database |
34,396 | public Clustering < M > run ( Database database , Relation < V > relation ) { if ( relation . size ( ) == 0 ) { throw new IllegalArgumentException ( "database empty: must contain elements" ) ; } List < ? extends EMClusterModel < M > > models = mfactory . buildInitialModels ( database , relation , k , SquaredEuclideanDi... | Performs the EM clustering algorithm on the given database . |
34,397 | public static void recomputeCovarianceMatrices ( Relation < ? extends NumberVector > relation , WritableDataStore < double [ ] > probClusterIGivenX , List < ? extends EMClusterModel < ? > > models , double prior ) { final int k = models . size ( ) ; boolean needsTwoPass = false ; for ( EMClusterModel < ? > m : models )... | Recompute the covariance matrixes . |
34,398 | public static double assignProbabilitiesToInstances ( Relation < ? extends NumberVector > relation , List < ? extends EMClusterModel < ? > > models , WritableDataStore < double [ ] > probClusterIGivenX ) { final int k = models . size ( ) ; double emSum = 0. ; for ( DBIDIter iditer = relation . iterDBIDs ( ) ; iditer . ... | Assigns the current probability values to the instances in the database and compute the expectation value of the current mixture of distributions . |
34,399 | protected synchronized void updateVisualizerMenus ( ) { Projection proj = null ; if ( svgCanvas . getPlot ( ) instanceof DetailView ) { PlotItem item = ( ( DetailView ) svgCanvas . getPlot ( ) ) . getPlotItem ( ) ; proj = item . proj ; } menubar . removeAll ( ) ; menubar . add ( filemenu ) ; ResultHierarchy hier = cont... | Update the visualizer menus . |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.