idx
int64 0
165k
| question
stringlengths 73
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stringlengths 5
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int64 21
890
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int64 3
255
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|---|---|---|---|---|
157,000
|
public void reInsert ( N node , IndexTreePath < E > path , int [ ] offs ) { final int depth = path . getPathCount ( ) ; long [ ] remove = BitsUtil . zero ( node . getCapacity ( ) ) ; List < E > reInsertEntries = new ArrayList <> ( offs . length ) ; for ( int i = 0 ; i < offs . length ; i ++ ) { reInsertEntries . add ( node . getEntry ( offs [ i ] ) ) ; BitsUtil . setI ( remove , offs [ i ] ) ; } // Remove the entries we reinsert node . removeMask ( remove ) ; writeNode ( node ) ; // and adapt the mbrs IndexTreePath < E > childPath = path ; N child = node ; while ( childPath . getParentPath ( ) != null ) { N parent = getNode ( childPath . getParentPath ( ) . getEntry ( ) ) ; int indexOfChild = childPath . getIndex ( ) ; if ( child . adjustEntry ( parent . getEntry ( indexOfChild ) ) ) { writeNode ( parent ) ; childPath = childPath . getParentPath ( ) ; child = parent ; } else { break ; // TODO: stop writing when MBR didn't change! } } // reinsert the first entries final Logging log = getLogger ( ) ; for ( E entry : reInsertEntries ) { if ( node . isLeaf ( ) ) { if ( log . isDebugging ( ) ) { log . debug ( "reinsert " + entry ) ; } insertLeafEntry ( entry ) ; } else { if ( log . isDebugging ( ) ) { log . debug ( "reinsert " + entry + " at " + depth ) ; } insertDirectoryEntry ( entry , depth ) ; } } }
|
Reinserts the specified node at the specified level .
| 400
| 11
|
157,001
|
private void condenseTree ( IndexTreePath < E > subtree , Stack < N > stack ) { N node = getNode ( subtree . getEntry ( ) ) ; // node is not root if ( ! isRoot ( node ) ) { N parent = getNode ( subtree . getParentPath ( ) . getEntry ( ) ) ; int index = subtree . getIndex ( ) ; if ( hasUnderflow ( node ) ) { if ( parent . deleteEntry ( index ) ) { stack . push ( node ) ; } else { node . adjustEntry ( parent . getEntry ( index ) ) ; } } else { node . adjustEntry ( parent . getEntry ( index ) ) ; } writeNode ( parent ) ; // get subtree to parent condenseTree ( subtree . getParentPath ( ) , stack ) ; } // node is root else { if ( hasUnderflow ( node ) && node . getNumEntries ( ) == 1 && ! node . isLeaf ( ) ) { N child = getNode ( node . getEntry ( 0 ) ) ; final N newRoot ; if ( child . isLeaf ( ) ) { newRoot = createNewLeafNode ( ) ; newRoot . setPageID ( getRootID ( ) ) ; for ( int i = 0 ; i < child . getNumEntries ( ) ; i ++ ) { newRoot . addLeafEntry ( child . getEntry ( i ) ) ; } } else { newRoot = createNewDirectoryNode ( ) ; newRoot . setPageID ( getRootID ( ) ) ; for ( int i = 0 ; i < child . getNumEntries ( ) ; i ++ ) { newRoot . addDirectoryEntry ( child . getEntry ( i ) ) ; } } writeNode ( newRoot ) ; height -- ; } } }
|
Condenses the tree after deletion of some nodes .
| 389
| 10
|
157,002
|
private void getLeafNodes ( N node , List < E > result , int currentLevel ) { // Level 1 are the leaf nodes, Level 2 is the one atop! if ( currentLevel == 2 ) { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { result . add ( node . getEntry ( i ) ) ; } } else { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { getLeafNodes ( getNode ( node . getEntry ( i ) ) , result , ( currentLevel - 1 ) ) ; } } }
|
Determines the entries pointing to the leaf nodes of the specified subtree .
| 137
| 16
|
157,003
|
public static double angleDense ( NumberVector v1 , NumberVector v2 ) { final int dim1 = v1 . getDimensionality ( ) , dim2 = v2 . getDimensionality ( ) ; final int mindim = ( dim1 <= dim2 ) ? dim1 : dim2 ; // Essentially, we want to compute this: // v1.transposeTimes(v2) / (v1.euclideanLength() * v2.euclideanLength()); // We can just compute all three in parallel. double cross = 0 , l1 = 0 , l2 = 0 ; for ( int k = 0 ; k < mindim ; k ++ ) { final double r1 = v1 . doubleValue ( k ) ; final double r2 = v2 . doubleValue ( k ) ; cross += r1 * r2 ; l1 += r1 * r1 ; l2 += r2 * r2 ; } for ( int k = mindim ; k < dim1 ; k ++ ) { final double r1 = v1 . doubleValue ( k ) ; l1 += r1 * r1 ; } for ( int k = mindim ; k < dim2 ; k ++ ) { final double r2 = v2 . doubleValue ( k ) ; l2 += r2 * r2 ; } final double a = ( cross == 0. ) ? 0. : // ( l1 == 0. || l2 == 0. ) ? 1. : // FastMath . sqrt ( ( cross / l1 ) * ( cross / l2 ) ) ; return ( a < 1. ) ? a : 1. ; }
|
Compute the absolute cosine of the angle between two dense vectors .
| 352
| 14
|
157,004
|
public static double angleSparse ( SparseNumberVector v1 , SparseNumberVector v2 ) { // TODO: exploit precomputed length, when available? double l1 = 0. , l2 = 0. , cross = 0. ; int i1 = v1 . iter ( ) , i2 = v2 . iter ( ) ; while ( v1 . iterValid ( i1 ) && v2 . iterValid ( i2 ) ) { final int d1 = v1 . iterDim ( i1 ) , d2 = v2 . iterDim ( i2 ) ; if ( d1 < d2 ) { final double val = v1 . iterDoubleValue ( i1 ) ; l1 += val * val ; i1 = v1 . iterAdvance ( i1 ) ; } else if ( d2 < d1 ) { final double val = v2 . iterDoubleValue ( i2 ) ; l2 += val * val ; i2 = v2 . iterAdvance ( i2 ) ; } else { // d1 == d2 final double val1 = v1 . iterDoubleValue ( i1 ) ; final double val2 = v2 . iterDoubleValue ( i2 ) ; l1 += val1 * val1 ; l2 += val2 * val2 ; cross += val1 * val2 ; i1 = v1 . iterAdvance ( i1 ) ; i2 = v2 . iterAdvance ( i2 ) ; } } while ( v1 . iterValid ( i1 ) ) { final double val = v1 . iterDoubleValue ( i1 ) ; l1 += val * val ; i1 = v1 . iterAdvance ( i1 ) ; } while ( v2 . iterValid ( i2 ) ) { final double val = v2 . iterDoubleValue ( i2 ) ; l2 += val * val ; i2 = v2 . iterAdvance ( i2 ) ; } final double a = ( cross == 0. ) ? 0. : // ( l1 == 0. || l2 == 0. ) ? 1. : // FastMath . sqrt ( ( cross / l1 ) * ( cross / l2 ) ) ; return ( a < 1. ) ? a : 1. ; }
|
Compute the angle for sparse vectors .
| 482
| 8
|
157,005
|
public static double angleSparseDense ( SparseNumberVector v1 , NumberVector v2 ) { // TODO: exploit precomputed length, when available. final int dim2 = v2 . getDimensionality ( ) ; double l1 = 0. , l2 = 0. , cross = 0. ; int i1 = v1 . iter ( ) , d2 = 0 ; while ( v1 . iterValid ( i1 ) ) { final int d1 = v1 . iterDim ( i1 ) ; while ( d2 < d1 && d2 < dim2 ) { final double val = v2 . doubleValue ( d2 ) ; l2 += val * val ; ++ d2 ; } if ( d2 < dim2 ) { assert ( d1 == d2 ) : "Dimensions not ordered" ; final double val1 = v1 . iterDoubleValue ( i1 ) ; final double val2 = v2 . doubleValue ( d2 ) ; l1 += val1 * val1 ; l2 += val2 * val2 ; cross += val1 * val2 ; i1 = v1 . iterAdvance ( i1 ) ; ++ d2 ; } else { final double val = v1 . iterDoubleValue ( i1 ) ; l1 += val * val ; i1 = v1 . iterAdvance ( i1 ) ; } } while ( d2 < dim2 ) { final double val = v2 . doubleValue ( d2 ) ; l2 += val * val ; ++ d2 ; } final double a = ( cross == 0. ) ? 0. : // ( l1 == 0. || l2 == 0. ) ? 1. : // FastMath . sqrt ( ( cross / l1 ) * ( cross / l2 ) ) ; return ( a < 1. ) ? a : 1. ; }
|
Compute the angle for a sparse and a dense vector .
| 397
| 12
|
157,006
|
public static double cosAngle ( NumberVector v1 , NumberVector v2 ) { // Java Hotspot appears to optimize these better than if-then-else: return v1 instanceof SparseNumberVector ? // v2 instanceof SparseNumberVector ? // angleSparse ( ( SparseNumberVector ) v1 , ( SparseNumberVector ) v2 ) : // angleSparseDense ( ( SparseNumberVector ) v1 , v2 ) : // v2 instanceof SparseNumberVector ? // angleSparseDense ( ( SparseNumberVector ) v2 , v1 ) : // angleDense ( v1 , v2 ) ; }
|
Compute the absolute cosine of the angle between two vectors .
| 141
| 13
|
157,007
|
public static double minCosAngle ( SpatialComparable v1 , SpatialComparable v2 ) { if ( v1 instanceof NumberVector && v2 instanceof NumberVector ) { return cosAngle ( ( NumberVector ) v1 , ( NumberVector ) v2 ) ; } final int dim1 = v1 . getDimensionality ( ) , dim2 = v2 . getDimensionality ( ) ; final int mindim = ( dim1 <= dim2 ) ? dim1 : dim2 ; // Essentially, we want to compute this: // absmax(v1.transposeTimes(v2))/(min(v1.euclideanLength())*min(v2.euclideanLength())); // We can just compute all three in parallel. double s1 = 0 , s2 = 0 , l1 = 0 , l2 = 0 ; for ( int k = 0 ; k < mindim ; k ++ ) { final double min1 = v1 . getMin ( k ) , max1 = v1 . getMax ( k ) ; final double min2 = v2 . getMin ( k ) , max2 = v2 . getMax ( k ) ; final double p1 = min1 * min2 , p2 = min1 * max2 ; final double p3 = max1 * min2 , p4 = max1 * max2 ; s1 += Math . max ( Math . max ( p1 , p2 ) , Math . max ( p3 , p4 ) ) ; s2 += Math . min ( Math . min ( p1 , p2 ) , Math . min ( p3 , p4 ) ) ; if ( max1 < 0 ) { l1 += max1 * max1 ; } else if ( min1 > 0 ) { l1 += min1 * min1 ; } // else: 0 if ( max2 < 0 ) { l2 += max2 * max2 ; } else if ( min2 > 0 ) { l2 += min2 * min2 ; } // else: 0 } for ( int k = mindim ; k < dim1 ; k ++ ) { final double min1 = v1 . getMin ( k ) , max1 = v1 . getMax ( k ) ; if ( max1 < 0. ) { l1 += max1 * max1 ; } else if ( min1 > 0. ) { l1 += min1 * min1 ; } // else: 0 } for ( int k = mindim ; k < dim2 ; k ++ ) { final double min2 = v2 . getMin ( k ) , max2 = v2 . getMax ( k ) ; if ( max2 < 0. ) { l2 += max2 * max2 ; } else if ( min2 > 0. ) { l2 += min2 * min2 ; } // else: 0 } final double cross = Math . max ( Math . abs ( s1 ) , Math . abs ( s2 ) ) ; final double a = ( cross == 0. ) ? 0. : // ( l1 == 0. || l2 == 0. ) ? 1. : // FastMath . sqrt ( ( cross / l1 ) * ( cross / l2 ) ) ; return ( a < 1. ) ? a : 1. ; }
|
Compute the minimum angle between two rectangles .
| 705
| 10
|
157,008
|
public static double angle ( NumberVector v1 , NumberVector v2 , NumberVector o ) { final int dim1 = v1 . getDimensionality ( ) , dim2 = v2 . getDimensionality ( ) , dimo = o . getDimensionality ( ) ; final int mindim = ( dim1 <= dim2 ) ? dim1 : dim2 ; // Essentially, we want to compute this: // v1' = v1 - o, v2' = v2 - o // v1'.transposeTimes(v2') / (v1'.euclideanLength()*v2'.euclideanLength()); // We can just compute all three in parallel. double cross = 0 , l1 = 0 , l2 = 0 ; for ( int k = 0 ; k < mindim ; k ++ ) { final double ok = k < dimo ? o . doubleValue ( k ) : 0. ; final double r1 = v1 . doubleValue ( k ) - ok ; final double r2 = v2 . doubleValue ( k ) - ok ; cross += r1 * r2 ; l1 += r1 * r1 ; l2 += r2 * r2 ; } for ( int k = mindim ; k < dim1 ; k ++ ) { final double ok = k < dimo ? o . doubleValue ( k ) : 0. ; final double r1 = v1 . doubleValue ( k ) - ok ; l1 += r1 * r1 ; } for ( int k = mindim ; k < dim2 ; k ++ ) { final double ok = k < dimo ? o . doubleValue ( k ) : 0. ; final double r2 = v2 . doubleValue ( k ) - ok ; l2 += r2 * r2 ; } final double a = ( cross == 0. ) ? 0. : // ( l1 == 0. || l2 == 0. ) ? 1. : // FastMath . sqrt ( ( cross / l1 ) * ( cross / l2 ) ) ; return ( a < 1. ) ? a : 1. ; }
|
Compute the angle between two vectors with respect to a reference point .
| 452
| 14
|
157,009
|
public static double dotDense ( NumberVector v1 , NumberVector v2 ) { final int dim1 = v1 . getDimensionality ( ) , dim2 = v2 . getDimensionality ( ) ; final int mindim = ( dim1 <= dim2 ) ? dim1 : dim2 ; double dot = 0 ; for ( int k = 0 ; k < mindim ; k ++ ) { dot += v1 . doubleValue ( k ) * v2 . doubleValue ( k ) ; } return dot ; }
|
Compute the dot product of two dense vectors .
| 111
| 10
|
157,010
|
public static double dotSparse ( SparseNumberVector v1 , SparseNumberVector v2 ) { double dot = 0. ; int i1 = v1 . iter ( ) , i2 = v2 . iter ( ) ; while ( v1 . iterValid ( i1 ) && v2 . iterValid ( i2 ) ) { final int d1 = v1 . iterDim ( i1 ) , d2 = v2 . iterDim ( i2 ) ; if ( d1 < d2 ) { i1 = v1 . iterAdvance ( i1 ) ; } else if ( d2 < d1 ) { i2 = v2 . iterAdvance ( i2 ) ; } else { // d1 == d2 dot += v1 . iterDoubleValue ( i1 ) * v2 . iterDoubleValue ( i2 ) ; i1 = v1 . iterAdvance ( i1 ) ; i2 = v2 . iterAdvance ( i2 ) ; } } return dot ; }
|
Compute the dot product for two sparse vectors .
| 215
| 10
|
157,011
|
public static double dotSparseDense ( SparseNumberVector v1 , NumberVector v2 ) { final int dim2 = v2 . getDimensionality ( ) ; double dot = 0. ; for ( int i1 = v1 . iter ( ) ; v1 . iterValid ( i1 ) ; ) { final int d1 = v1 . iterDim ( i1 ) ; if ( d1 >= dim2 ) { break ; } dot += v1 . iterDoubleValue ( i1 ) * v2 . doubleValue ( d1 ) ; i1 = v1 . iterAdvance ( i1 ) ; } return dot ; }
|
Compute the dot product for a sparse and a dense vector .
| 137
| 13
|
157,012
|
public static double dot ( NumberVector v1 , NumberVector v2 ) { // Java Hotspot appears to optimize these better than if-then-else: return v1 instanceof SparseNumberVector ? // v2 instanceof SparseNumberVector ? // dotSparse ( ( SparseNumberVector ) v1 , ( SparseNumberVector ) v2 ) : // dotSparseDense ( ( SparseNumberVector ) v1 , v2 ) : // v2 instanceof SparseNumberVector ? // dotSparseDense ( ( SparseNumberVector ) v2 , v1 ) : // dotDense ( v1 , v2 ) ; }
|
Compute the dot product of the angle between two vectors .
| 139
| 12
|
157,013
|
public static double minDot ( SpatialComparable v1 , SpatialComparable v2 ) { if ( v1 instanceof NumberVector && v2 instanceof NumberVector ) { return dot ( ( NumberVector ) v1 , ( NumberVector ) v2 ) ; } final int dim1 = v1 . getDimensionality ( ) , dim2 = v2 . getDimensionality ( ) ; final int mindim = ( dim1 <= dim2 ) ? dim1 : dim2 ; // Essentially, we want to compute this: // absmax(v1.transposeTimes(v2)); double s1 = 0 , s2 = 0 ; for ( int k = 0 ; k < mindim ; k ++ ) { final double min1 = v1 . getMin ( k ) , max1 = v1 . getMax ( k ) ; final double min2 = v2 . getMin ( k ) , max2 = v2 . getMax ( k ) ; final double p1 = min1 * min2 , p2 = min1 * max2 ; final double p3 = max1 * min2 , p4 = max1 * max2 ; s1 += Math . max ( Math . max ( p1 , p2 ) , Math . max ( p3 , p4 ) ) ; s2 += Math . min ( Math . min ( p1 , p2 ) , Math . min ( p3 , p4 ) ) ; } return Math . max ( Math . abs ( s1 ) , Math . abs ( s2 ) ) ; }
|
Compute the minimum angle between two rectangles assuming unit length vectors
| 331
| 13
|
157,014
|
public static < V extends NumberVector > V project ( V v , long [ ] selectedAttributes , NumberVector . Factory < V > factory ) { int card = BitsUtil . cardinality ( selectedAttributes ) ; if ( factory instanceof SparseNumberVector . Factory ) { final SparseNumberVector . Factory < ? > sfactory = ( SparseNumberVector . Factory < ? > ) factory ; Int2DoubleOpenHashMap values = new Int2DoubleOpenHashMap ( card , .8f ) ; for ( int d = BitsUtil . nextSetBit ( selectedAttributes , 0 ) ; d >= 0 ; d = BitsUtil . nextSetBit ( selectedAttributes , d + 1 ) ) { if ( v . doubleValue ( d ) != 0.0 ) { values . put ( d , v . doubleValue ( d ) ) ; } } // We can't avoid this cast, because Java doesn't know that V is a // SparseNumberVector: @ SuppressWarnings ( "unchecked" ) V projectedVector = ( V ) sfactory . newNumberVector ( values , card ) ; return projectedVector ; } else { double [ ] newAttributes = new double [ card ] ; int i = 0 ; for ( int d = BitsUtil . nextSetBit ( selectedAttributes , 0 ) ; d >= 0 ; d = BitsUtil . nextSetBit ( selectedAttributes , d + 1 ) ) { newAttributes [ i ] = v . doubleValue ( d ) ; i ++ ; } return factory . newNumberVector ( newAttributes ) ; } }
|
Project a number vector to the specified attributes .
| 333
| 9
|
157,015
|
public void mergeWith ( Core o ) { o . num = this . num = ( num < o . num ? num : o . num ) ; }
|
Merge two cores .
| 32
| 5
|
157,016
|
public Clustering < Model > run ( Relation < ? > relation ) { HashMap < String , DBIDs > labelMap = multiple ? multipleAssignment ( relation ) : singleAssignment ( relation ) ; ModifiableDBIDs noiseids = DBIDUtil . newArray ( ) ; Clustering < Model > result = new Clustering <> ( "By Label Clustering" , "bylabel-clustering" ) ; for ( Entry < String , DBIDs > entry : labelMap . entrySet ( ) ) { DBIDs ids = entry . getValue ( ) ; if ( ids . size ( ) <= 1 ) { noiseids . addDBIDs ( ids ) ; continue ; } // Build a cluster Cluster < Model > c = new Cluster < Model > ( entry . getKey ( ) , ids , ClusterModel . CLUSTER ) ; if ( noisepattern != null && noisepattern . matcher ( entry . getKey ( ) ) . find ( ) ) { c . setNoise ( true ) ; } result . addToplevelCluster ( c ) ; } // Collected noise IDs. if ( noiseids . size ( ) > 0 ) { Cluster < Model > c = new Cluster < Model > ( "Noise" , noiseids , ClusterModel . CLUSTER ) ; c . setNoise ( true ) ; result . addToplevelCluster ( c ) ; } return result ; }
|
Run the actual clustering algorithm .
| 313
| 7
|
157,017
|
private HashMap < String , DBIDs > singleAssignment ( Relation < ? > data ) { HashMap < String , DBIDs > labelMap = new HashMap <> ( ) ; for ( DBIDIter iditer = data . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { final Object val = data . get ( iditer ) ; String label = ( val != null ) ? val . toString ( ) : null ; assign ( labelMap , label , iditer ) ; } return labelMap ; }
|
Assigns the objects of the database to single clusters according to their labels .
| 118
| 16
|
157,018
|
private HashMap < String , DBIDs > multipleAssignment ( Relation < ? > data ) { HashMap < String , DBIDs > labelMap = new HashMap <> ( ) ; for ( DBIDIter iditer = data . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { String [ ] labels = data . get ( iditer ) . toString ( ) . split ( " " ) ; for ( String label : labels ) { assign ( labelMap , label , iditer ) ; } } return labelMap ; }
|
Assigns the objects of the database to multiple clusters according to their labels .
| 121
| 16
|
157,019
|
private void assign ( HashMap < String , DBIDs > labelMap , String label , DBIDRef id ) { if ( labelMap . containsKey ( label ) ) { DBIDs exist = labelMap . get ( label ) ; if ( exist instanceof DBID ) { ModifiableDBIDs n = DBIDUtil . newHashSet ( ) ; n . add ( ( DBID ) exist ) ; n . add ( id ) ; labelMap . put ( label , n ) ; } else { assert ( exist instanceof HashSetModifiableDBIDs ) ; assert ( exist . size ( ) > 1 ) ; ( ( ModifiableDBIDs ) exist ) . add ( id ) ; } } else { labelMap . put ( label , DBIDUtil . deref ( id ) ) ; } }
|
Assigns the specified id to the labelMap according to its label
| 169
| 14
|
157,020
|
public void put ( double val ) { min = val < min ? val : min ; max = val > max ? val : max ; }
|
Process a single double value .
| 29
| 6
|
157,021
|
public static void addShadowFilter ( SVGPlot svgp ) { Element shadow = svgp . getIdElement ( SHADOW_ID ) ; if ( shadow == null ) { shadow = svgp . svgElement ( SVGConstants . SVG_FILTER_TAG ) ; shadow . setAttribute ( SVGConstants . SVG_ID_ATTRIBUTE , SHADOW_ID ) ; shadow . setAttribute ( SVGConstants . SVG_WIDTH_ATTRIBUTE , "140%" ) ; shadow . setAttribute ( SVGConstants . SVG_HEIGHT_ATTRIBUTE , "140%" ) ; Element offset = svgp . svgElement ( SVGConstants . SVG_FE_OFFSET_TAG ) ; offset . setAttribute ( SVGConstants . SVG_IN_ATTRIBUTE , SVGConstants . SVG_SOURCE_ALPHA_VALUE ) ; offset . setAttribute ( SVGConstants . SVG_RESULT_ATTRIBUTE , "off" ) ; offset . setAttribute ( SVGConstants . SVG_DX_ATTRIBUTE , "0.1" ) ; offset . setAttribute ( SVGConstants . SVG_DY_ATTRIBUTE , "0.1" ) ; shadow . appendChild ( offset ) ; Element gauss = svgp . svgElement ( SVGConstants . SVG_FE_GAUSSIAN_BLUR_TAG ) ; gauss . setAttribute ( SVGConstants . SVG_IN_ATTRIBUTE , "off" ) ; gauss . setAttribute ( SVGConstants . SVG_RESULT_ATTRIBUTE , "blur" ) ; gauss . setAttribute ( SVGConstants . SVG_STD_DEVIATION_ATTRIBUTE , "0.1" ) ; shadow . appendChild ( gauss ) ; Element blend = svgp . svgElement ( SVGConstants . SVG_FE_BLEND_TAG ) ; blend . setAttribute ( SVGConstants . SVG_IN_ATTRIBUTE , SVGConstants . SVG_SOURCE_GRAPHIC_VALUE ) ; blend . setAttribute ( SVGConstants . SVG_IN2_ATTRIBUTE , "blur" ) ; blend . setAttribute ( SVGConstants . SVG_MODE_ATTRIBUTE , SVGConstants . SVG_NORMAL_VALUE ) ; shadow . appendChild ( blend ) ; svgp . getDefs ( ) . appendChild ( shadow ) ; svgp . putIdElement ( SHADOW_ID , shadow ) ; } }
|
Static method to prepare a SVG document for drop shadow effects .
| 544
| 12
|
157,022
|
public static void addLightGradient ( SVGPlot svgp ) { Element gradient = svgp . getIdElement ( LIGHT_GRADIENT_ID ) ; if ( gradient == null ) { gradient = svgp . svgElement ( SVGConstants . SVG_LINEAR_GRADIENT_TAG ) ; gradient . setAttribute ( SVGConstants . SVG_ID_ATTRIBUTE , LIGHT_GRADIENT_ID ) ; gradient . setAttribute ( SVGConstants . SVG_X1_ATTRIBUTE , "0" ) ; gradient . setAttribute ( SVGConstants . SVG_Y1_ATTRIBUTE , "0" ) ; gradient . setAttribute ( SVGConstants . SVG_X2_ATTRIBUTE , "0" ) ; gradient . setAttribute ( SVGConstants . SVG_Y2_ATTRIBUTE , "1" ) ; Element stop0 = svgp . svgElement ( SVGConstants . SVG_STOP_TAG ) ; stop0 . setAttribute ( SVGConstants . SVG_STOP_COLOR_ATTRIBUTE , "white" ) ; stop0 . setAttribute ( SVGConstants . SVG_STOP_OPACITY_ATTRIBUTE , "1" ) ; stop0 . setAttribute ( SVGConstants . SVG_OFFSET_ATTRIBUTE , "0" ) ; gradient . appendChild ( stop0 ) ; Element stop04 = svgp . svgElement ( SVGConstants . SVG_STOP_TAG ) ; stop04 . setAttribute ( SVGConstants . SVG_STOP_COLOR_ATTRIBUTE , "white" ) ; stop04 . setAttribute ( SVGConstants . SVG_STOP_OPACITY_ATTRIBUTE , "0" ) ; stop04 . setAttribute ( SVGConstants . SVG_OFFSET_ATTRIBUTE , ".4" ) ; gradient . appendChild ( stop04 ) ; Element stop06 = svgp . svgElement ( SVGConstants . SVG_STOP_TAG ) ; stop06 . setAttribute ( SVGConstants . SVG_STOP_COLOR_ATTRIBUTE , "black" ) ; stop06 . setAttribute ( SVGConstants . SVG_STOP_OPACITY_ATTRIBUTE , "0" ) ; stop06 . setAttribute ( SVGConstants . SVG_OFFSET_ATTRIBUTE , ".6" ) ; gradient . appendChild ( stop06 ) ; Element stop1 = svgp . svgElement ( SVGConstants . SVG_STOP_TAG ) ; stop1 . setAttribute ( SVGConstants . SVG_STOP_COLOR_ATTRIBUTE , "black" ) ; stop1 . setAttribute ( SVGConstants . SVG_STOP_OPACITY_ATTRIBUTE , ".5" ) ; stop1 . setAttribute ( SVGConstants . SVG_OFFSET_ATTRIBUTE , "1" ) ; gradient . appendChild ( stop1 ) ; svgp . getDefs ( ) . appendChild ( gradient ) ; svgp . putIdElement ( LIGHT_GRADIENT_ID , gradient ) ; } }
|
Static method to prepare a SVG document for light gradient effects .
| 678
| 12
|
157,023
|
public static Element makeCheckmark ( SVGPlot svgp ) { Element checkmark = svgp . svgElement ( SVGConstants . SVG_PATH_TAG ) ; checkmark . setAttribute ( SVGConstants . SVG_D_ATTRIBUTE , SVG_CHECKMARK_PATH ) ; checkmark . setAttribute ( SVGConstants . SVG_FILL_ATTRIBUTE , SVGConstants . CSS_BLACK_VALUE ) ; checkmark . setAttribute ( SVGConstants . SVG_STROKE_ATTRIBUTE , SVGConstants . CSS_NONE_VALUE ) ; return checkmark ; }
|
Creates a 15x15 big checkmark
| 133
| 9
|
157,024
|
public double continueToMargin ( double [ ] origin , double [ ] delta ) { assert ( delta . length == 2 && origin . length == 2 ) ; double factor = Double . POSITIVE_INFINITY ; if ( delta [ 0 ] > 0 ) { factor = Math . min ( factor , ( maxx - origin [ 0 ] ) / delta [ 0 ] ) ; } else if ( delta [ 0 ] < 0 ) { factor = Math . min ( factor , ( origin [ 0 ] - minx ) / - delta [ 0 ] ) ; } if ( delta [ 1 ] > 0 ) { factor = Math . min ( factor , ( maxy - origin [ 1 ] ) / delta [ 1 ] ) ; } else if ( delta [ 1 ] < 0 ) { factor = Math . min ( factor , ( origin [ 1 ] - miny ) / - delta [ 1 ] ) ; } return factor ; }
|
Continue a line along a given direction to the margin .
| 194
| 11
|
157,025
|
@ Override public void clear ( ) { try { file . setLength ( header . size ( ) ) ; } catch ( IOException e ) { throw new RuntimeException ( e ) ; } }
|
Clears this PageFile .
| 41
| 6
|
157,026
|
private double deviation ( double [ ] delta , double [ ] [ ] beta ) { final double a = squareSum ( delta ) ; final double b = squareSum ( transposeTimes ( beta , delta ) ) ; return ( a > b ) ? FastMath . sqrt ( a - b ) : 0. ; }
|
Deviation from a manifold described by beta .
| 66
| 9
|
157,027
|
private Separation findSeparation ( Relation < NumberVector > relation , DBIDs currentids , int dimension , Random r ) { Separation separation = new Separation ( ) ; // determine the number of samples needed, to secure that with a specific // probability // in at least on sample every sampled point is from the same cluster. int samples = ( int ) Math . min ( LOG_NOT_FROM_ONE_CLUSTER_PROBABILITY / ( FastMath . log1p ( - FastMath . powFast ( samplingLevel , - dimension ) ) ) , ( double ) currentids . size ( ) ) ; // System.out.println("Number of samples: " + samples); int remaining_retries = 100 ; for ( int i = 1 ; i <= samples ; i ++ ) { DBIDs sample = DBIDUtil . randomSample ( currentids , dimension + 1 , r ) ; final DBIDIter iter = sample . iter ( ) ; // Use first as origin double [ ] originV = relation . get ( iter ) . toArray ( ) ; iter . advance ( ) ; // Build orthogonal basis from remainder double [ ] [ ] basis ; { List < double [ ] > vectors = new ArrayList <> ( sample . size ( ) - 1 ) ; for ( ; iter . valid ( ) ; iter . advance ( ) ) { double [ ] vec = relation . get ( iter ) . toArray ( ) ; vectors . add ( minusEquals ( vec , originV ) ) ; } // generate orthogonal basis basis = generateOrthonormalBasis ( vectors ) ; if ( basis == null ) { // new sample has to be taken. i -- ; if ( -- remaining_retries < 0 ) { throw new TooManyRetriesException ( "Too many retries in sampling, and always a linear dependant data set." ) ; } continue ; } } // Generate and fill a histogram. DoubleDynamicHistogram histogram = new DoubleDynamicHistogram ( BINS ) ; double w = 1.0 / currentids . size ( ) ; for ( DBIDIter iter2 = currentids . iter ( ) ; iter2 . valid ( ) ; iter2 . advance ( ) ) { // Skip sampled points if ( sample . contains ( iter2 ) ) { continue ; } double [ ] vec = minusEquals ( relation . get ( iter2 ) . toArray ( ) , originV ) ; final double distance = deviation ( vec , basis ) ; histogram . increment ( distance , w ) ; } double [ ] th = findAndEvaluateThreshold ( histogram ) ; // evaluate threshold if ( th [ 1 ] > separation . goodness ) { separation . goodness = th [ 1 ] ; separation . threshold = th [ 0 ] ; separation . originV = originV ; separation . basis = basis ; } } return separation ; }
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This method samples a number of linear manifolds an tries to determine which the one with the best cluster is .
| 605
| 22
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157,028
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public double getDistance ( final DBIDRef o1 , final DBIDRef o2 ) { return FastMath . sqrt ( getSquaredDistance ( o1 , o2 ) ) ; }
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Returns the kernel distance between the two specified objects .
| 41
| 10
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157,029
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public double getSquaredDistance ( final DBIDRef id1 , final DBIDRef id2 ) { final int o1 = idmap . getOffset ( id1 ) , o2 = idmap . getOffset ( id2 ) ; return kernel [ o1 ] [ o1 ] + kernel [ o2 ] [ o2 ] - 2 * kernel [ o1 ] [ o2 ] ; }
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Returns the squared kernel distance between the two specified objects .
| 84
| 11
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157,030
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public double getSimilarity ( DBIDRef id1 , DBIDRef id2 ) { return kernel [ idmap . getOffset ( id1 ) ] [ idmap . getOffset ( id2 ) ] ; }
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Get the kernel similarity for the given objects .
| 45
| 9
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157,031
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protected double [ ] [ ] initialMeans ( Database database , Relation < V > relation ) { Duration inittime = getLogger ( ) . newDuration ( initializer . getClass ( ) + ".time" ) . begin ( ) ; double [ ] [ ] means = initializer . chooseInitialMeans ( database , relation , k , getDistanceFunction ( ) ) ; getLogger ( ) . statistics ( inittime . end ( ) ) ; return means ; }
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Choose the initial means .
| 101
| 5
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157,032
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public static void plusEquals ( double [ ] sum , NumberVector vec ) { for ( int d = 0 ; d < sum . length ; d ++ ) { sum [ d ] += vec . doubleValue ( d ) ; } }
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Similar to VMath . plusEquals but accepts a number vector .
| 49
| 14
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157,033
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public static void minusEquals ( double [ ] sum , NumberVector vec ) { for ( int d = 0 ; d < sum . length ; d ++ ) { sum [ d ] -= vec . doubleValue ( d ) ; } }
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Similar to VMath . minusEquals but accepts a number vector .
| 49
| 14
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157,034
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public static void plusMinusEquals ( double [ ] add , double [ ] sub , NumberVector vec ) { for ( int d = 0 ; d < add . length ; d ++ ) { final double v = vec . doubleValue ( d ) ; add [ d ] += v ; sub [ d ] -= v ; } }
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Add to one remove from another .
| 69
| 7
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157,035
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protected static void incrementalUpdateMean ( double [ ] mean , NumberVector vec , int newsize , double op ) { if ( newsize == 0 ) { return ; // Keep old mean } // Note: numerically stabilized version: VMath . plusTimesEquals ( mean , VMath . minusEquals ( vec . toArray ( ) , mean ) , op / newsize ) ; }
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Compute an incremental update for the mean .
| 82
| 9
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157,036
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public static int fastModPrime ( long data ) { // Mix high and low 32 bit: int high = ( int ) ( data >>> 32 ) ; // Use fast multiplication with 5 for high: int alpha = ( ( int ) data ) + ( high << 2 + high ) ; // Note that in Java, PRIME will be negative. if ( alpha < 0 && alpha > - 5 ) { alpha = alpha + 5 ; } return alpha ; }
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Fast modulo operation for the largest unsigned integer prime .
| 93
| 11
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157,037
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private void doRangeQuery ( DBID o_p , AbstractMTreeNode < O , ? , ? > node , O q , double r_q , ModifiableDoubleDBIDList result ) { double d1 = 0. ; if ( o_p != null ) { d1 = distanceQuery . distance ( o_p , q ) ; index . statistics . countDistanceCalculation ( ) ; } if ( ! node . isLeaf ( ) ) { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { MTreeEntry entry = node . getEntry ( i ) ; DBID o_r = entry . getRoutingObjectID ( ) ; double r_or = entry . getCoveringRadius ( ) ; double d2 = o_p != null ? entry . getParentDistance ( ) : 0. ; double diff = Math . abs ( d1 - d2 ) ; double sum = r_q + r_or ; if ( diff <= sum ) { double d3 = distanceQuery . distance ( o_r , q ) ; index . statistics . countDistanceCalculation ( ) ; if ( d3 <= sum ) { AbstractMTreeNode < O , ? , ? > child = index . getNode ( ( ( DirectoryEntry ) entry ) . getPageID ( ) ) ; doRangeQuery ( o_r , child , q , r_q , result ) ; } } } } else { for ( int i = 0 ; i < node . getNumEntries ( ) ; i ++ ) { MTreeEntry entry = node . getEntry ( i ) ; DBID o_j = entry . getRoutingObjectID ( ) ; double d2 = o_p != null ? entry . getParentDistance ( ) : 0. ; double diff = Math . abs ( d1 - d2 ) ; if ( diff <= r_q ) { double d3 = distanceQuery . distance ( o_j , q ) ; index . statistics . countDistanceCalculation ( ) ; if ( d3 <= r_q ) { result . add ( d3 , o_j ) ; } } } } }
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Performs a range query on the specified subtree . It recursively traverses all paths from the specified node which cannot be excluded from leading to qualifying objects .
| 458
| 33
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157,038
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public static double pdf ( double x , double mu , double beta ) { final double z = ( x - mu ) / beta ; if ( x == Double . NEGATIVE_INFINITY ) { return 0. ; } return FastMath . exp ( - z - FastMath . exp ( - z ) ) / beta ; }
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PDF of Gumbel distribution
| 69
| 6
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157,039
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public static double logpdf ( double x , double mu , double beta ) { if ( x == Double . NEGATIVE_INFINITY ) { return Double . NEGATIVE_INFINITY ; } final double z = ( x - mu ) / beta ; return - z - FastMath . exp ( - z ) - FastMath . log ( beta ) ; }
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log PDF of Gumbel distribution
| 77
| 7
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157,040
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public static double cdf ( double val , double mu , double beta ) { return FastMath . exp ( - FastMath . exp ( - ( val - mu ) / beta ) ) ; }
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CDF of Gumbel distribution
| 40
| 7
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157,041
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public static double quantile ( double val , double mu , double beta ) { return mu - beta * FastMath . log ( - FastMath . log ( val ) ) ; }
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Quantile function of Gumbel distribution
| 37
| 8
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157,042
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public void setPartitions ( Relation < V > relation ) throws IllegalArgumentException { if ( ( FastMath . log ( partitions ) / FastMath . log ( 2 ) ) != ( int ) ( FastMath . log ( partitions ) / FastMath . log ( 2 ) ) ) { throw new IllegalArgumentException ( "Number of partitions must be a power of 2!" ) ; } final int dimensions = RelationUtil . dimensionality ( relation ) ; final int size = relation . size ( ) ; splitPositions = new double [ dimensions ] [ partitions + 1 ] ; for ( int d = 0 ; d < dimensions ; d ++ ) { double [ ] tempdata = new double [ size ] ; int j = 0 ; for ( DBIDIter iditer = relation . iterDBIDs ( ) ; iditer . valid ( ) ; iditer . advance ( ) ) { tempdata [ j ] = relation . get ( iditer ) . doubleValue ( d ) ; j += 1 ; } Arrays . sort ( tempdata ) ; for ( int b = 0 ; b < partitions ; b ++ ) { int start = ( int ) ( b * size / ( double ) partitions ) ; splitPositions [ d ] [ b ] = tempdata [ start ] ; } // make sure that last object will be included splitPositions [ d ] [ partitions ] = tempdata [ size - 1 ] + 0.000001 ; } }
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Initialize the data set grid by computing quantiles .
| 302
| 11
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157,043
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public long getScannedPages ( ) { int vacapacity = pageSize / VectorApproximation . byteOnDisk ( splitPositions . length , partitions ) ; long vasize = ( long ) Math . ceil ( ( vectorApprox . size ( ) ) / ( 1.0 * vacapacity ) ) ; return vasize * scans ; }
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Get the number of scanned bytes .
| 75
| 7
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157,044
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private void hqr2BackTransformation ( int nn , int low , int high ) { for ( int j = nn - 1 ; j >= low ; j -- ) { final int last = j < high ? j : high ; for ( int i = low ; i <= high ; i ++ ) { final double [ ] Vi = V [ i ] ; double sum = 0. ; for ( int k = low ; k <= last ; k ++ ) { sum += Vi [ k ] * H [ k ] [ j ] ; } Vi [ j ] = sum ; } } }
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Back transformation to get eigenvectors of original matrix .
| 124
| 12
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157,045
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protected static double gammaQuantileNewtonRefinement ( final double logpt , final double k , final double theta , final int maxit , double x ) { final double EPS_N = 1e-15 ; // Precision threshold // 0 is not possible, try MIN_NORMAL instead if ( x <= 0 ) { x = Double . MIN_NORMAL ; } // Current estimation double logpc = logcdf ( x , k , theta ) ; if ( x == Double . MIN_NORMAL && logpc > logpt * ( 1. + 1e-7 ) ) { return 0. ; } if ( logpc == Double . NEGATIVE_INFINITY ) { return 0. ; } // Refine by newton iterations for ( int i = 0 ; i < maxit ; i ++ ) { // Error of current approximation final double logpe = logpc - logpt ; if ( Math . abs ( logpe ) < Math . abs ( EPS_N * logpt ) ) { break ; } // Step size is controlled by PDF: final double g = logpdf ( x , k , theta ) ; if ( g == Double . NEGATIVE_INFINITY ) { break ; } final double newx = x - logpe * FastMath . exp ( logpc - g ) ; // New estimate: logpc = logcdf ( newx , k , theta ) ; if ( Math . abs ( logpc - logpt ) > Math . abs ( logpe ) || ( i > 0 && Math . abs ( logpc - logpt ) == Math . abs ( logpe ) ) ) { // no further improvement break ; } x = newx ; } return x ; }
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Refinement of ChiSquared probit using Newton iterations .
| 361
| 12
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157,046
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@ Override public Element useMarker ( SVGPlot plot , Element parent , double x , double y , int stylenr , double size ) { Element marker = plot . svgCircle ( x , y , size * .5 ) ; final String col ; if ( stylenr == - 1 ) { col = dotcolor ; } else if ( stylenr == - 2 ) { col = greycolor ; } else { col = colors . getColor ( stylenr ) ; } SVGUtil . setStyle ( marker , SVGConstants . CSS_FILL_PROPERTY + ":" + col ) ; parent . appendChild ( marker ) ; return marker ; }
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Use a given marker on the document .
| 143
| 8
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157,047
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public Clustering < DendrogramModel > run ( PointerHierarchyRepresentationResult pointerresult ) { Clustering < DendrogramModel > result = new Instance ( pointerresult ) . run ( ) ; result . addChildResult ( pointerresult ) ; return result ; }
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Process an existing result .
| 61
| 5
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157,048
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public static double erf ( double x ) { final double w = x < 0 ? - x : x ; double y ; if ( w < 2.2 ) { double t = w * w ; int k = ( int ) t ; t -= k ; k *= 13 ; y = ( ( ( ( ( ( ( ( ( ( ( ( ERF_COEFF1 [ k ] * t + ERF_COEFF1 [ k + 1 ] ) * t + // ERF_COEFF1 [ k + 2 ] ) * t + ERF_COEFF1 [ k + 3 ] ) * t + ERF_COEFF1 [ k + 4 ] ) * t + // ERF_COEFF1 [ k + 5 ] ) * t + ERF_COEFF1 [ k + 6 ] ) * t + ERF_COEFF1 [ k + 7 ] ) * t + // ERF_COEFF1 [ k + 8 ] ) * t + ERF_COEFF1 [ k + 9 ] ) * t + ERF_COEFF1 [ k + 10 ] ) * t + // ERF_COEFF1 [ k + 11 ] ) * t + ERF_COEFF1 [ k + 12 ] ) * w ; } else if ( w < 6.9 ) { int k = ( int ) w ; double t = w - k ; k = 13 * ( k - 2 ) ; y = ( ( ( ( ( ( ( ( ( ( ( ERF_COEFF2 [ k ] * t + ERF_COEFF2 [ k + 1 ] ) * t + // ERF_COEFF2 [ k + 2 ] ) * t + ERF_COEFF2 [ k + 3 ] ) * t + ERF_COEFF2 [ k + 4 ] ) * t + // ERF_COEFF2 [ k + 5 ] ) * t + ERF_COEFF2 [ k + 6 ] ) * t + ERF_COEFF2 [ k + 7 ] ) * t + // ERF_COEFF2 [ k + 8 ] ) * t + ERF_COEFF2 [ k + 9 ] ) * t + ERF_COEFF2 [ k + 10 ] ) * t + // ERF_COEFF2 [ k + 11 ] ) * t + ERF_COEFF2 [ k + 12 ] ; y *= y ; y *= y ; y *= y ; y = 1 - y * y ; } else if ( w == w ) { y = 1 ; } else { return Double . NaN ; } return x < 0 ? - y : y ; }
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Error function for Gaussian distributions = Normal distributions .
| 568
| 10
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157,049
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public static double standardNormalQuantile ( double d ) { return ( d == 0 ) ? Double . NEGATIVE_INFINITY : // ( d == 1 ) ? Double . POSITIVE_INFINITY : // ( Double . isNaN ( d ) || d < 0 || d > 1 ) ? Double . NaN // : MathUtil . SQRT2 * - erfcinv ( 2 * d ) ; }
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Approximate the inverse error function for normal distributions .
| 91
| 11
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157,050
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@ Override public < N extends SpatialComparable > List < List < N > > partition ( List < N > spatialObjects , int minEntries , int maxEntries ) { List < List < N >> partitions = new ArrayList <> ( ) ; List < N > objects = new ArrayList <> ( spatialObjects ) ; while ( ! objects . isEmpty ( ) ) { StringBuilder msg = new StringBuilder ( ) ; // get the split axis and split point int splitAxis = chooseMaximalExtendedSplitAxis ( objects ) ; int splitPoint = chooseBulkSplitPoint ( objects . size ( ) , minEntries , maxEntries ) ; if ( LOG . isDebugging ( ) ) { msg . append ( "\nsplitAxis " ) . append ( splitAxis ) ; msg . append ( "\nsplitPoint " ) . append ( splitPoint ) ; } // sort in the right dimension Collections . sort ( objects , new SpatialSingleMinComparator ( splitAxis ) ) ; // insert into partition List < N > partition1 = new ArrayList <> ( ) ; for ( int i = 0 ; i < splitPoint ; i ++ ) { N o = objects . remove ( 0 ) ; partition1 . add ( o ) ; } partitions . add ( partition1 ) ; // copy array if ( LOG . isDebugging ( ) ) { msg . append ( "\ncurrent partition " ) . append ( partition1 ) ; msg . append ( "\nremaining objects # " ) . append ( objects . size ( ) ) ; LOG . debugFine ( msg . toString ( ) ) ; } } if ( LOG . isDebugging ( ) ) { LOG . debugFine ( "partitions " + partitions ) ; } return partitions ; }
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Partitions the specified feature vectors where the split axes are the dimensions with maximum extension .
| 379
| 17
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157,051
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private int chooseMaximalExtendedSplitAxis ( List < ? extends SpatialComparable > objects ) { // maximum and minimum value for the extension int dimension = objects . get ( 0 ) . getDimensionality ( ) ; double [ ] maxExtension = new double [ dimension ] ; double [ ] minExtension = new double [ dimension ] ; Arrays . fill ( minExtension , Double . MAX_VALUE ) ; // compute min and max value in each dimension for ( SpatialComparable object : objects ) { for ( int d = 0 ; d < dimension ; d ++ ) { double min , max ; min = object . getMin ( d ) ; max = object . getMax ( d ) ; if ( maxExtension [ d ] < max ) { maxExtension [ d ] = max ; } if ( minExtension [ d ] > min ) { minExtension [ d ] = min ; } } } // set split axis to dim with maximal extension int splitAxis = - 1 ; double max = 0 ; for ( int d = 0 ; d < dimension ; d ++ ) { double currentExtension = maxExtension [ d ] - minExtension [ d ] ; if ( max < currentExtension ) { max = currentExtension ; splitAxis = d ; } } return splitAxis ; }
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Computes and returns the best split axis . The best split axis is the split axes with the maximal extension .
| 281
| 22
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157,052
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public void setTotal ( int total ) throws IllegalArgumentException { if ( getProcessed ( ) > total ) { throw new IllegalArgumentException ( getProcessed ( ) + " exceeds total: " + total ) ; } this . total = total ; }
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Modify the total value .
| 55
| 6
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157,053
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@ SuppressWarnings ( "unchecked" ) protected < T > T get ( DBIDRef id , int index ) { Object [ ] d = data . get ( DBIDUtil . deref ( id ) ) ; if ( d == null ) { return null ; } return ( T ) d [ index ] ; }
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Actual getter .
| 70
| 5
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157,054
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@ SuppressWarnings ( "unchecked" ) protected < T > T set ( DBIDRef id , int index , T value ) { Object [ ] d = data . get ( DBIDUtil . deref ( id ) ) ; if ( d == null ) { d = new Object [ rlen ] ; data . put ( DBIDUtil . deref ( id ) , d ) ; } T ret = ( T ) d [ index ] ; d [ index ] = value ; return ret ; }
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Actual setter .
| 109
| 5
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157,055
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public UniformDistribution estimate ( DoubleMinMax mm ) { return new UniformDistribution ( Math . max ( mm . getMin ( ) , - Double . MAX_VALUE ) , Math . min ( mm . getMax ( ) , Double . MAX_VALUE ) ) ; }
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Estimate parameters from minimum and maximum observed .
| 57
| 9
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157,056
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public static boolean canVisualize ( Relation < ? > rel , AbstractMTree < ? , ? , ? , ? > tree ) { if ( ! TypeUtil . NUMBER_VECTOR_FIELD . isAssignableFromType ( rel . getDataTypeInformation ( ) ) ) { return false ; } return getLPNormP ( tree ) > 0 ; }
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Test for a visualizable index in the context s database .
| 80
| 12
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157,057
|
void initializeRandomAttributes ( SimpleTypeInformation < V > in ) { int d = ( ( VectorFieldTypeInformation < V > ) in ) . getDimensionality ( ) ; selectedAttributes = BitsUtil . random ( k , d , rnd . getSingleThreadedRandom ( ) ) ; }
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Initialize random attributes .
| 63
| 5
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157,058
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protected void singleEnsemble ( final double [ ] ensemble , final NumberVector vec ) { double [ ] buf = new double [ 1 ] ; for ( int i = 0 ; i < ensemble . length ; i ++ ) { buf [ 0 ] = vec . doubleValue ( i ) ; ensemble [ i ] = voting . combine ( buf , 1 ) ; if ( Double . isNaN ( ensemble [ i ] ) ) { LOG . warning ( "NaN after combining: " + FormatUtil . format ( buf ) + " " + voting . toString ( ) ) ; } } applyScaling ( ensemble , scaling ) ; }
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Build a single - element ensemble .
| 132
| 7
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157,059
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public static String getFullDescription ( Parameter < ? > param ) { StringBuilder description = new StringBuilder ( 1000 ) // . append ( param . getShortDescription ( ) ) . append ( FormatUtil . NEWLINE ) ; param . describeValues ( description ) ; if ( ! FormatUtil . endsWith ( description , FormatUtil . NEWLINE ) ) { description . append ( FormatUtil . NEWLINE ) ; } if ( param . hasDefaultValue ( ) ) { description . append ( "Default: " ) . append ( param . getDefaultValueAsString ( ) ) . append ( FormatUtil . NEWLINE ) ; } List < ? extends ParameterConstraint < ? > > constraints = param . getConstraints ( ) ; if ( constraints != null && ! constraints . isEmpty ( ) ) { description . append ( ( constraints . size ( ) == 1 ) ? "Constraint: " : "Constraints: " ) // . append ( constraints . get ( 0 ) . getDescription ( param . getOptionID ( ) . getName ( ) ) ) ; for ( int i = 1 ; i < constraints . size ( ) ; i ++ ) { description . append ( ", " ) . append ( constraints . get ( i ) . getDescription ( param . getOptionID ( ) . getName ( ) ) ) ; } description . append ( FormatUtil . NEWLINE ) ; } return description . toString ( ) ; }
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Format a parameter description .
| 308
| 5
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157,060
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private static void println ( StringBuilder buf , int width , String data ) { for ( String line : FormatUtil . splitAtLastBlank ( data , width ) ) { buf . append ( line ) ; if ( ! line . endsWith ( FormatUtil . NEWLINE ) ) { buf . append ( FormatUtil . NEWLINE ) ; } } }
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Simple writing helper with no indentation .
| 76
| 8
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157,061
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public static int centroids ( Relation < ? extends NumberVector > rel , List < ? extends Cluster < ? > > clusters , NumberVector [ ] centroids , NoiseHandling noiseOption ) { assert ( centroids . length == clusters . size ( ) ) ; int ignorednoise = 0 ; Iterator < ? extends Cluster < ? > > ci = clusters . iterator ( ) ; for ( int i = 0 ; ci . hasNext ( ) ; i ++ ) { Cluster < ? > cluster = ci . next ( ) ; if ( cluster . size ( ) <= 1 || cluster . isNoise ( ) ) { switch ( noiseOption ) { case IGNORE_NOISE : ignorednoise += cluster . size ( ) ; case TREAT_NOISE_AS_SINGLETONS : centroids [ i ] = null ; continue ; case MERGE_NOISE : break ; // Treat as cluster below } } centroids [ i ] = ModelUtil . getPrototypeOrCentroid ( cluster . getModel ( ) , rel , cluster . getIDs ( ) ) ; } return ignorednoise ; }
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Compute centroids .
| 242
| 6
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157,062
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public static double cdf ( double val , double rate ) { final double v = .5 * FastMath . exp ( - rate * Math . abs ( val ) ) ; return ( v == Double . POSITIVE_INFINITY ) ? ( ( val <= 0 ) ? 0 : 1 ) : // ( val < 0 ) ? v : 1 - v ; }
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Cumulative density static version
| 77
| 6
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157,063
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protected double maxDistance ( DoubleDBIDList elems ) { double max = 0 ; for ( DoubleDBIDListIter it = elems . iter ( ) ; it . valid ( ) ; it . advance ( ) ) { final double v = it . doubleValue ( ) ; max = max > v ? max : v ; } return max ; }
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Find maximum in a list via scanning .
| 73
| 8
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157,064
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protected void excludeNotCovered ( ModifiableDoubleDBIDList candidates , double fmax , ModifiableDoubleDBIDList collect ) { for ( DoubleDBIDListIter it = candidates . iter ( ) ; it . valid ( ) ; ) { if ( it . doubleValue ( ) > fmax ) { collect . add ( it . doubleValue ( ) , it ) ; candidates . removeSwap ( it . getOffset ( ) ) ; } else { it . advance ( ) ; // Keep in candidates } } }
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Retain all elements within the current cover .
| 108
| 9
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157,065
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protected void collectByCover ( DBIDRef cur , ModifiableDoubleDBIDList candidates , double fmax , ModifiableDoubleDBIDList collect ) { assert ( collect . size ( ) == 0 ) : "Not empty" ; DoubleDBIDListIter it = candidates . iter ( ) . advance ( ) ; // Except first = cur! while ( it . valid ( ) ) { assert ( ! DBIDUtil . equal ( cur , it ) ) ; final double dist = distance ( cur , it ) ; if ( dist <= fmax ) { // Collect collect . add ( dist , it ) ; candidates . removeSwap ( it . getOffset ( ) ) ; } else { it . advance ( ) ; // Keep in candidates, outside cover radius. } } }
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Collect all elements with respect to a new routing object .
| 161
| 11
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157,066
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private void process ( double [ ] data , double min , double max , KernelDensityFunction kernel , int window , double epsilon ) { dens = new double [ data . length ] ; var = new double [ data . length ] ; // This is the desired bandwidth of the kernel. double halfwidth = ( ( max - min ) / window ) * .5 ; for ( int current = 0 ; current < data . length ; current ++ ) { double value = 0.0 ; for ( int i = current ; i >= 0 ; i -- ) { double delta = Math . abs ( data [ i ] - data [ current ] ) / halfwidth ; final double contrib = kernel . density ( delta ) ; value += contrib ; if ( contrib < epsilon ) { break ; } } for ( int i = current + 1 ; i < data . length ; i ++ ) { double delta = Math . abs ( data [ i ] - data [ current ] ) / halfwidth ; final double contrib = kernel . density ( delta ) ; value += contrib ; if ( contrib < epsilon ) { break ; } } double realwidth = ( Math . min ( data [ current ] + halfwidth , max ) - Math . max ( min , data [ current ] - halfwidth ) ) ; double weight = realwidth / ( 2 * halfwidth ) ; dens [ current ] = value / ( data . length * realwidth * .5 ) ; var [ current ] = 1 / weight ; } }
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Process a new array
| 318
| 4
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157,067
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public static double [ ] computeSimilarityMatrix ( DependenceMeasure sim , Relation < ? extends NumberVector > rel ) { final int dim = RelationUtil . dimensionality ( rel ) ; // TODO: we could use less memory (no copy), but this would likely be // slower. Maybe as a fallback option? double [ ] [ ] data = new double [ dim ] [ rel . size ( ) ] ; int r = 0 ; for ( DBIDIter it = rel . iterDBIDs ( ) ; it . valid ( ) ; it . advance ( ) , r ++ ) { NumberVector v = rel . get ( it ) ; for ( int d = 0 ; d < dim ; d ++ ) { data [ d ] [ r ] = v . doubleValue ( d ) ; } } return sim . dependence ( DoubleArrayAdapter . STATIC , Arrays . asList ( data ) ) ; }
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Compute a column - wise dependency matrix for the given relation .
| 192
| 13
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157,068
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protected N buildSpanningTree ( int dim , double [ ] mat , Layout layout ) { assert ( layout . edges == null || layout . edges . size ( ) == 0 ) ; int [ ] iedges = PrimsMinimumSpanningTree . processDense ( mat , new LowerTriangularAdapter ( dim ) ) ; int root = findOptimalRoot ( iedges ) ; // Convert edges: ArrayList < Edge > edges = new ArrayList <> ( iedges . length >> 1 ) ; for ( int i = 1 ; i < iedges . length ; i += 2 ) { edges . add ( new Edge ( iedges [ i - 1 ] , iedges [ i ] ) ) ; } layout . edges = edges ; // Prefill nodes array with nulls. ArrayList < N > nodes = new ArrayList <> ( dim ) ; for ( int i = 0 ; i < dim ; i ++ ) { nodes . add ( null ) ; } layout . nodes = nodes ; N rootnode = buildTree ( iedges , root , - 1 , nodes ) ; return rootnode ; }
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Build the minimum spanning tree .
| 239
| 6
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157,069
|
protected N buildTree ( int [ ] msg , int cur , int parent , ArrayList < N > nodes ) { // Count the number of children: int c = 0 ; for ( int i = 1 ; i < msg . length ; i += 2 ) { if ( ( msg [ i - 1 ] == cur && msg [ i ] != parent ) || ( msg [ i ] == cur && msg [ i - 1 ] != parent ) ) { c ++ ; } } // Build children: List < N > children = Collections . emptyList ( ) ; if ( c > 0 ) { children = new ArrayList <> ( c ) ; for ( int i = 1 ; i < msg . length ; i += 2 ) { if ( msg [ i - 1 ] == cur && msg [ i ] != parent ) { children . add ( buildTree ( msg , msg [ i ] , cur , nodes ) ) ; } else if ( msg [ i ] == cur && msg [ i - 1 ] != parent ) { children . add ( buildTree ( msg , msg [ i - 1 ] , cur , nodes ) ) ; } } } N node = makeNode ( cur , children ) ; nodes . set ( cur , node ) ; return node ; }
|
Recursive tree build method .
| 260
| 6
|
157,070
|
protected int maxDepth ( Layout . Node node ) { int depth = 0 ; for ( int i = 0 ; i < node . numChildren ( ) ; i ++ ) { depth = Math . max ( depth , maxDepth ( node . getChild ( i ) ) ) ; } return depth + 1 ; }
|
Compute the depth of the graph .
| 64
| 8
|
157,071
|
@ Override public void initialize ( ) { TreeIndexHeader header = createHeader ( ) ; if ( this . file . initialize ( header ) ) { initializeFromFile ( header , file ) ; } rootEntry = createRootEntry ( ) ; }
|
Initialize the tree if the page file already existed .
| 51
| 11
|
157,072
|
public N getNode ( int nodeID ) { if ( nodeID == getPageID ( rootEntry ) ) { return getRoot ( ) ; } else { return file . readPage ( nodeID ) ; } }
|
Returns the node with the specified id .
| 45
| 8
|
157,073
|
public void initializeFromFile ( TreeIndexHeader header , PageFile < N > file ) { this . dirCapacity = header . getDirCapacity ( ) ; this . leafCapacity = header . getLeafCapacity ( ) ; this . dirMinimum = header . getDirMinimum ( ) ; this . leafMinimum = header . getLeafMinimum ( ) ; if ( getLogger ( ) . isDebugging ( ) ) { StringBuilder msg = new StringBuilder ( ) ; msg . append ( getClass ( ) ) ; msg . append ( "\n file = " ) . append ( file . getClass ( ) ) ; getLogger ( ) . debugFine ( msg . toString ( ) ) ; } this . initialized = true ; }
|
Initializes this index from an existing persistent file .
| 158
| 10
|
157,074
|
protected final void initialize ( E exampleLeaf ) { initializeCapacities ( exampleLeaf ) ; // create empty root createEmptyRoot ( exampleLeaf ) ; final Logging log = getLogger ( ) ; if ( log . isStatistics ( ) ) { String cls = this . getClass ( ) . getName ( ) ; log . statistics ( new LongStatistic ( cls + ".directory.capacity" , dirCapacity ) ) ; log . statistics ( new LongStatistic ( cls + ".directory.minfill" , dirMinimum ) ) ; log . statistics ( new LongStatistic ( cls + ".leaf.capacity" , leafCapacity ) ) ; log . statistics ( new LongStatistic ( cls + ".leaf.minfill" , leafMinimum ) ) ; } initialized = true ; }
|
Initializes the index .
| 173
| 5
|
157,075
|
public static MeanVarianceMinMax [ ] newArray ( int dimensionality ) { MeanVarianceMinMax [ ] arr = new MeanVarianceMinMax [ dimensionality ] ; for ( int i = 0 ; i < dimensionality ; i ++ ) { arr [ i ] = new MeanVarianceMinMax ( ) ; } return arr ; }
|
Create and initialize a new array of MeanVarianceMinMax
| 72
| 12
|
157,076
|
@ Override public double getWeight ( double distance , double max , double stddev ) { if ( stddev <= 0 ) { return 1 ; } double scaleddistance = distance / stddev ; return stddev * FastMath . exp ( - .5 * scaleddistance ) ; }
|
Get exponential weight max is ignored .
| 62
| 7
|
157,077
|
protected static < A > int [ ] sortedIndex ( final NumberArrayAdapter < ? , A > adapter , final A data , int len ) { int [ ] s1 = MathUtil . sequence ( 0 , len ) ; IntegerArrayQuickSort . sort ( s1 , ( x , y ) -> Double . compare ( adapter . getDouble ( data , x ) , adapter . getDouble ( data , y ) ) ) ; return s1 ; }
|
Build a sorted index of objects .
| 94
| 7
|
157,078
|
protected static < A > int [ ] discretize ( NumberArrayAdapter < ? , A > adapter , A data , final int len , final int bins ) { double min = adapter . getDouble ( data , 0 ) , max = min ; for ( int i = 1 ; i < len ; i ++ ) { double v = adapter . getDouble ( data , i ) ; if ( v < min ) { min = v ; } else if ( v > max ) { max = v ; } } final double scale = ( max > min ) ? bins / ( max - min ) : 1 ; int [ ] discData = new int [ len ] ; for ( int i = 0 ; i < len ; i ++ ) { int bin = ( int ) Math . floor ( ( adapter . getDouble ( data , i ) - min ) * scale ) ; discData [ i ] = bin < 0 ? 0 : bin >= bins ? bins - 1 : bin ; } return discData ; }
|
Discretize a data set into equi - width bin numbers .
| 207
| 14
|
157,079
|
protected void finishGridRow ( ) { GridBagConstraints constraints = new GridBagConstraints ( ) ; constraints . gridwidth = GridBagConstraints . REMAINDER ; constraints . weightx = 0 ; final JLabel icon ; if ( param . isOptional ( ) ) { if ( param . isDefined ( ) && param . tookDefaultValue ( ) && ! ( param instanceof Flag ) ) { // TODO: better icon for default value? icon = new JLabel ( StockIcon . getStockIcon ( StockIcon . DIALOG_INFORMATION ) ) ; icon . setToolTipText ( "Default value: " + param . getDefaultValueAsString ( ) ) ; } else { icon = new JLabel ( ) ; icon . setMinimumSize ( new Dimension ( 16 , 16 ) ) ; } } else { if ( ! param . isDefined ( ) ) { icon = new JLabel ( StockIcon . getStockIcon ( StockIcon . DIALOG_ERROR ) ) ; icon . setToolTipText ( "Missing value." ) ; } else { icon = new JLabel ( ) ; icon . setMinimumSize ( new Dimension ( 16 , 16 ) ) ; } } parent . add ( icon , constraints ) ; }
|
Complete the current grid row adding the icon at the end
| 268
| 11
|
157,080
|
private double normalize ( int d , double val ) { d = ( mean . length == 1 ) ? 0 : d ; return ( val - mean [ d ] ) / stddev [ d ] ; }
|
Normalize a single dimension .
| 44
| 6
|
157,081
|
private static EigenPair [ ] processDecomposition ( EigenvalueDecomposition evd ) { double [ ] eigenvalues = evd . getRealEigenvalues ( ) ; double [ ] [ ] eigenvectors = evd . getV ( ) ; EigenPair [ ] eigenPairs = new EigenPair [ eigenvalues . length ] ; for ( int i = 0 ; i < eigenvalues . length ; i ++ ) { double e = Math . abs ( eigenvalues [ i ] ) ; double [ ] v = VMath . getCol ( eigenvectors , i ) ; eigenPairs [ i ] = new EigenPair ( v , e ) ; } Arrays . sort ( eigenPairs , Comparator . reverseOrder ( ) ) ; return eigenPairs ; }
|
Convert an eigenvalue decomposition into EigenPair objects .
| 182
| 15
|
157,082
|
public void nextIteration ( double [ ] [ ] means ) { this . means = means ; changed = false ; final int k = means . length ; final int dim = means [ 0 ] . length ; centroids = new double [ k ] [ dim ] ; sizes = new int [ k ] ; Arrays . fill ( varsum , 0. ) ; }
|
Initialize for a new iteration .
| 78
| 7
|
157,083
|
public double [ ] [ ] getMeans ( ) { double [ ] [ ] newmeans = new double [ centroids . length ] [ ] ; for ( int i = 0 ; i < centroids . length ; i ++ ) { if ( sizes [ i ] == 0 ) { newmeans [ i ] = means [ i ] ; // Keep old mean. continue ; } newmeans [ i ] = centroids [ i ] ; } return newmeans ; }
|
Get the new means .
| 103
| 5
|
157,084
|
public static String format ( double [ ] v , int w , int d ) { DecimalFormat format = new DecimalFormat ( ) ; format . setDecimalFormatSymbols ( new DecimalFormatSymbols ( Locale . US ) ) ; format . setMinimumIntegerDigits ( 1 ) ; format . setMaximumFractionDigits ( d ) ; format . setMinimumFractionDigits ( d ) ; format . setGroupingUsed ( false ) ; int width = w + 1 ; StringBuilder msg = new StringBuilder ( ) // . append ( ' ' ) ; // start on new line. for ( int i = 0 ; i < v . length ; i ++ ) { String s = format . format ( v [ i ] ) ; // format the number // At _least_ 1 whitespace is added whitespace ( msg , Math . max ( 1 , width - s . length ( ) ) ) . append ( s ) ; } return msg . toString ( ) ; }
|
Returns a string representation of this vector .
| 210
| 8
|
157,085
|
public static StringBuilder formatTo ( StringBuilder buf , double [ ] d , String sep ) { if ( d == null ) { return buf . append ( "null" ) ; } if ( d . length == 0 ) { return buf ; } buf . append ( d [ 0 ] ) ; for ( int i = 1 ; i < d . length ; i ++ ) { buf . append ( sep ) . append ( d [ i ] ) ; } return buf ; }
|
Formats the double array d with the default number format .
| 98
| 12
|
157,086
|
public static String format ( float [ ] f ) { return ( f == null ) ? "null" : ( f . length == 0 ) ? "" : // formatTo ( new StringBuilder ( ) , f , ", " ) . toString ( ) ; }
|
Formats the float array f with as separator and default precision .
| 54
| 14
|
157,087
|
public static String format ( int [ ] a , String sep ) { return ( a == null ) ? "null" : ( a . length == 0 ) ? "" : // formatTo ( new StringBuilder ( ) , a , sep ) . toString ( ) ; }
|
Formats the int array a for printing purposes .
| 56
| 10
|
157,088
|
public static String format ( boolean [ ] b , final String sep ) { return ( b == null ) ? "null" : ( b . length == 0 ) ? "" : // formatTo ( new StringBuilder ( ) , b , ", " ) . toString ( ) ; }
|
Formats the boolean array b with as separator .
| 58
| 11
|
157,089
|
public static String format ( double [ ] [ ] d ) { return d == null ? "null" : ( d . length == 0 ) ? "[]" : // formatTo ( new StringBuilder ( ) . append ( "[\n" ) , d , " [" , "]\n" , ", " , NF2 ) . append ( ' ' ) . toString ( ) ; }
|
Formats the double array d with as separator and 2 fraction digits .
| 81
| 15
|
157,090
|
public static String format ( double [ ] [ ] m , int w , int d , String pre , String pos , String csep ) { DecimalFormat format = new DecimalFormat ( ) ; format . setDecimalFormatSymbols ( new DecimalFormatSymbols ( Locale . US ) ) ; format . setMinimumIntegerDigits ( 1 ) ; format . setMaximumFractionDigits ( d ) ; format . setMinimumFractionDigits ( d ) ; format . setGroupingUsed ( false ) ; StringBuilder msg = new StringBuilder ( ) ; for ( int i = 0 ; i < m . length ; i ++ ) { double [ ] row = m [ i ] ; msg . append ( pre ) ; for ( int j = 0 ; j < row . length ; j ++ ) { if ( j > 0 ) { msg . append ( csep ) ; } String s = format . format ( row [ j ] ) ; // format the number whitespace ( msg , w - s . length ( ) ) . append ( s ) ; } msg . append ( pos ) ; } return msg . toString ( ) ; }
|
Returns a string representation of this matrix .
| 244
| 8
|
157,091
|
public static String format ( double [ ] [ ] m , NumberFormat nf ) { return formatTo ( new StringBuilder ( ) . append ( "[\n" ) , m , " [" , "]\n" , ", " , nf ) . append ( "]" ) . toString ( ) ; }
|
returns String - representation of Matrix .
| 66
| 8
|
157,092
|
public static String format ( Collection < String > d , String sep ) { if ( d == null ) { return "null" ; } if ( d . isEmpty ( ) ) { return "" ; } if ( d . size ( ) == 1 ) { return d . iterator ( ) . next ( ) ; } int len = sep . length ( ) * ( d . size ( ) - 1 ) ; for ( String s : d ) { len += s . length ( ) ; } Iterator < String > it = d . iterator ( ) ; StringBuilder buffer = new StringBuilder ( len ) // . append ( it . next ( ) ) ; while ( it . hasNext ( ) ) { buffer . append ( sep ) . append ( it . next ( ) ) ; } return buffer . toString ( ) ; }
|
Formats the String collection with the specified separator .
| 171
| 11
|
157,093
|
public static String format ( String [ ] d , String sep ) { if ( d == null ) { return "null" ; } if ( d . length == 0 ) { return "" ; } if ( d . length == 1 ) { return d [ 0 ] ; } int len = sep . length ( ) * ( d . length - 1 ) ; for ( String s : d ) { len += s . length ( ) ; } StringBuilder buffer = new StringBuilder ( len ) // . append ( d [ 0 ] ) ; for ( int i = 1 ; i < d . length ; i ++ ) { buffer . append ( sep ) . append ( d [ i ] ) ; } return buffer . toString ( ) ; }
|
Formats the string array d with the specified separator .
| 152
| 12
|
157,094
|
public static int findSplitpoint ( String s , int width ) { // the newline (or EOS) is the fallback split position. int in = s . indexOf ( NEWLINE ) ; in = in < 0 ? s . length ( ) : in ; // Good enough? if ( in < width ) { return in ; } // otherwise, search for whitespace int iw = s . lastIndexOf ( ' ' , width ) ; // good whitespace found? if ( iw >= 0 && iw < width ) { return iw ; } // sub-optimal splitpoint - retry AFTER the given position int bp = nextPosition ( s . indexOf ( ' ' , width ) , s . indexOf ( NEWLINE , width ) ) ; if ( bp >= 0 ) { return bp ; } // even worse - can't split! return s . length ( ) ; }
|
Find the first space before position w or if there is none after w .
| 191
| 15
|
157,095
|
public static List < String > splitAtLastBlank ( String s , int width ) { List < String > chunks = new ArrayList <> ( ) ; String tmp = s ; while ( tmp . length ( ) > 0 ) { int index = findSplitpoint ( tmp , width ) ; // store first part chunks . add ( tmp . substring ( 0 , index ) ) ; // skip whitespace at beginning of line while ( index < tmp . length ( ) && tmp . charAt ( index ) == ' ' ) { index += 1 ; } // remove a newline if ( index < tmp . length ( ) && tmp . regionMatches ( index , NEWLINE , 0 , NEWLINE . length ( ) ) ) { index += NEWLINE . length ( ) ; } if ( index >= tmp . length ( ) ) { break ; } tmp = tmp . substring ( index ) ; } return chunks ; }
|
Splits the specified string at the last blank before width . If there is no blank before the given width it is split at the next .
| 191
| 28
|
157,096
|
public static String pad ( String o , int len ) { return o . length ( ) >= len ? o : ( o + whitespace ( len - o . length ( ) ) ) ; }
|
Pad a string to a given length by adding whitespace to the right .
| 40
| 15
|
157,097
|
public static String padRightAligned ( String o , int len ) { return o . length ( ) >= len ? o : ( whitespace ( len - o . length ( ) ) + o ) ; }
|
Pad a string to a given length by adding whitespace to the left .
| 43
| 15
|
157,098
|
public static String formatTimeDelta ( long time , CharSequence sep ) { final StringBuilder sb = new StringBuilder ( ) ; final Formatter fmt = new Formatter ( sb ) ; for ( int i = TIME_UNIT_SIZES . length - 1 ; i >= 0 ; -- i ) { // We do not include ms if we are in the order of minutes. if ( i == 0 && sb . length ( ) > 4 ) { continue ; } // Separator if ( sb . length ( ) > 0 ) { sb . append ( sep ) ; } final long acValue = time / TIME_UNIT_SIZES [ i ] ; time = time % TIME_UNIT_SIZES [ i ] ; if ( ! ( acValue == 0 && sb . length ( ) == 0 ) ) { fmt . format ( "%0" + TIME_UNIT_DIGITS [ i ] + "d%s" , Long . valueOf ( acValue ) , TIME_UNIT_NAMES [ i ] ) ; } } fmt . close ( ) ; return sb . toString ( ) ; }
|
Formats a time delta in human readable format .
| 246
| 10
|
157,099
|
public static StringBuilder appendZeros ( StringBuilder buf , int zeros ) { for ( int i = zeros ; i > 0 ; i -= ZEROPADDING . length ) { buf . append ( ZEROPADDING , 0 , i < ZEROPADDING . length ? i : ZEROPADDING . length ) ; } return buf ; }
|
Append zeros to a buffer .
| 78
| 8
|
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