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K=10,T=0.8: <data>np from scipy . linalg import block _ diag class lane tracker : def _ _ init _ _ ( self , n _ lanes , proc _ noise _ scale , meas _ noise _ scale , process _ cov _ parallel = 0 , proc _ noise _ type = ' white ' ) : self . n _ lanes = n _ lanes self . meas _ size = 4 * self . n _ lanes self . state _ size = self . meas _ size * 2 self . <UNK> _ size = 0 self </data>. <UNK> _ size = self . <UNK> _ size self . <UNK> _ size = 1 self . <UNK> _ size = 0 self . <UNK> _ size = 4 self . <UNK> _ size = 0 def _ _ init _ _ ( self , sample _ size , num _ bins , seed , seed , seed , seed , seed , seed ) : self . sample _ size = sample _ size self . sample _ size = sample _ size self . sample _ size = 0 self . sample _ size self . 
K=10,T=0.8: <data>cmd : tune = ' [ - - tune : interval : 1 0 ] ' elif ' record ' in cmd and c . any _ in ( ( ' - b ' , ' - j ' ) , cmd ) : tune = ' - - tune : sample : 3 ' if ' pebs ' in msg else ' [ - - tune : sample : 2 ] ' elif len ( tune ) : tune = ' [ - - tune : % s : 1 ] ' % tune if ' stacks ' in </data>cmd : tune = ' [ - tune : % s ] ' elif ' <UNK> ' in cmd or c . any _ in ( ' - b ' , ' - - tune : % s ' % tune ) else : source = ' [ - tune ] ' elif ' <UNK> ' in cmd : tune = ' % s ' % tune = ' [ - tune : % s ] ' % tune = ' [ - tune ] ' elif ' <UNK> ' in cmd : tune = ' ' else : tune 
K=10,T=0.8: <data>join ( outputdir , ' result _ { } . npy ' . format ( i ) ) , res ) parser = argparse . argument parser ( description = ' convert a story to graph ' ) parser . add _ argument ( " <UNK> " , help = " story filename " ) parser . add _ argument ( " outputdir " , help = " output directory " ) if _ _ name _ _ = = ' _ _ main _ _ ' : args = vars ( parser . parse _ args ( ) ) </data>parser . add _ argument ( " - r " , help = " output directory " , help = " output directory " ) parser . add _ argument ( " - r " , help = " output directory " ) args = parser . parse _ args ( ) if args . gpu = = ' 0 ' : args . gpu = args . gpu * args . gpu * args . gpu * args . gpu * args . gpu * args . gpu * args . gpu * args . gpu * args . 
K=10,T=0.8: <data>{ border - collapse : collapse ; background - color : table , th , td { border : 1 px solid th , td { padding : 0 . 2 em ; } td { min - width : 3 0 px ; } th { background - color : tr : nth - child ( odd ) { background - color : . <UNK> { columns : 2 3 0 0 px ; } . katex { font - size : 1 . 1 em ; color : black ; } . katex - display { margin - </data>color : { background - color : . <UNK> } " , } ) ) return self . layout . add ( ) def draw ( self , text : str , * * kwargs ) - > none : return self . layout . add ( ) class <UNK> ( object ) : def _ _ init _ _ ( self , * kwargs , * * kwargs , * * kwargs ) - > none : super ( <UNK> , self ) . _ _ init _ _ ( * kwargs , * * kwargs , * * 
K=10,T=0.8: <data>1 . 0 , 0 . 0 , 0 . 0 , hits else : precision , recall = correct / total , correct / len ( answers ) f 1 = 2 . 0 / ( 1 . 0 / precision + 1 . 0 / recall ) if precision ! = 0 and recall ! = 0 else 0 . 0 return precision , recall , f 1 , hits def compare _ pr ( kb _ pred _ file , doc _ pred _ file , hybrid _ pred _ file , w _ kb , eps </data>_ threshold , threshold _ threshold ) : if not threshold _ threshold > threshold _ threshold : threshold _ threshold , threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ threshold _ 
K=10,T=0.8: <data>save _ path , " w " , encoding = " utf - 8 " ) fin . write ( json . dumps ( test _ data , indent = 4 , ensure _ ascii = false ) ) fin . close ( ) if _ _ name _ _ = = ' _ _ main _ _ ' : content _ path _ dir = " data _ dir / train _ text . txt " title _ path _ dir = " data _ dir / train _ label . txt " train _ save _ path _ </data>dir = " data _ dir / test _ label . txt " data _ dir = " data " data _ dir = " data _ dir / test _ label . txt " train _ save _ path _ dir _ dir = " data _ dir / test _ label . txt " data _ dir = " data _ dir / train _ label . txt " test _ save _ path _ dir = " data _ dir / test _ label . txt " test _ save _ path _ dir = " data 
K=10,T=0.8: <data>: b = false if context . space _ data . tree _ type = = ' shader node tree ' : b = true return b node _ categories = [ kaleidoscope category ( " <UNK> " , " kaleidoscope " , items = [ node item ( " kaleidoscope _ hybrid . node " ) , node item ( " spectrum _ palette . node " ) , node item ( " intensity . node " ) ] ) , ] class kaleidoscope ( bpy . types . addon preferences ) : bl _ idname = " kaleidoscope </data>_ spectrum _ props . color _ string " bl _ label = " kaleidoscope _ spectrum _ props . color _ string " bl _ label = " kaleidoscope _ spectrum _ props . color _ string " bl _ description = " kaleidoscope _ spectrum _ props . color _ string " bl _ label = " kaleidoscope _ spectrum _ props . color _ string " bl _ label = " kaleidoscope _ spectrum _ props . color _ string " bl _ label = " kaleidoscope _ spectrum _ props . color _ string " bl 
K=10,T=0.8: <data>' ' : ' 1 0 ' , ' ' : ' 1 1 ' , ' ' : ' 1 2 ' , ' ' : ' 1 5 ' , ' ' : ' 3 ' , ' ' : ' 1 0 ' , ' ' : ' 1 2 ' , ' ' : ' 1 2 ' , ' ' : ' 1 2 ' , ' ' : ' 1 2 ' , ' ' : ' 1 5 ' , ' ' : ' 1 6 ' , ' ' : ' 1 </data>4 ' , ' ' : ' 1 4 ' , ' ' : ' 1 4 ' , ' ' : ' 1 4 ' , ' ' : ' 1 4 ' , ' ' : ' 2 4 ' , ' ' : ' 1 5 ' , ' ' : ' 6 ' , ' ' : ' 6 ' , ' ' : ' 6 ' , ' ' : ' 6 ' , ' ' : ' 6 ' , ' ' : ' 2 4 ' , ' ' : ' 7 ' 
K=10,T=0.8: <data>_ = visualize _ depth _ numpy ( depth _ map . numpy ( ) , near _ far ) rgb _ map = ( rgb _ map . numpy ( ) * 2 5 5 ) . astype ( ' uint 8 ' ) rgb _ maps . append ( rgb _ map ) depth _ maps . append ( depth _ map ) if save path is not none : imageio . imwrite ( f ' { save path } / { prtx } { idx : 0 3 d } . png ' , rgb _ map </data>) depth _ map . append ( depth _ map ) return depth _ maps def save _ depth _ numpy ( depth _ map ) : if not os . path . exists ( depth _ map ) : os . makedirs ( depth _ map ) return np . array ( depth _ map ) def save _ depth _ numpy ( depth _ map ) : os . makedirs ( depth _ map ) np . savetxt ( depth _ map ) np . savetxt ( depth _ map ) np . savetxt ( ' depth _