Диссертация (1137066), страница 14
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43-54.[85] Kaytoue, M., Kuznetsov S.O., Napoli,A.: Revisiting numerical pattern miningwith formal concept analysis. In: IJCAI, pp. 134201347 (2011).[86] https://www.investopedia.com/slide-show/top-bank-failures/84[87] http://www.banki.ru/news/lenta/?id=10257511[88] http://www.bbc.com/russian/news-42363290[89] https://www.bis.org/publ/bcbsca.htm85AppendixThe program code in R is provided for both continuous target prediction (QueryBased Regression Algorithm) and binary target prediction (Query Based Classification Algorithm) as for the prototype. It must be mentioned that for-loops are noteffective within R language and the following code serves as a working concept.The R language was chosen as soon as it is a vectorized language and, therefore,allows the reader quickly acquire understanding of algorithms discussed within thisthesis. If applying in real systems one is strongly recommended to use doParallel andfuture packages from CRAN or, better, different languages such as Java or C.The QBRA code is as follows:meet .
o p e r a t o r = f u n c t i o n ( x , y ) { # x and y a s . d a t a . f r a m e s w i t h named c o l u m n sr e s = d a t a . frame ( l b = 0 , ub = 0 , s t r i n g s A s F a c t o r s = FALSE ) [ − 1 , ]for ( i in 1: ncol ( x ) ) {r e s [ nrow ( r e s ) + 1 , ] = c ( min ( r b i n d ( x , y ) [ , i ] ) , max ( r b i n d ( x , y ) [ , i ] ) )}row . names ( r e s ) = names ( x )res}image .
f i n d = f u n c t i o n ( d e s c , d a t a ) { # d e s c a s p a t t e r n s t r u c t u r er e s = which ( d a t a [ , 1 ] >= d e s c [ 1 , " l b " ] & d a t a [ , 1 ] <= d e s c [ 1 , " ub " ] )f o r ( i i n 2 : nrow ( d e s c ) ) {r e s = i n t e r s e c t ( r e s , which ( d a t a [ , i ] >= d e s c [ i , " l b " ] & d a t a [ , i ] <= d e s c [ i , "ub " ] ) )}res}l a z y . e v a l u a t o r = f u n c t i o n ( v l , t r , # Datat a r g e t = " r r _ av " , v a r s = names ( t r ) [ names ( t r ) ! = t a r g e t ] , #Key f i e l d ss u b s a m p l e . s i z e = 0 .
0 1 , n . i t e r =1000 , a l p h a . t h r e s h o l d =0.01 ,a l l o w e d . d r o p o u t = 0 , c a p p e d = TRUE ,a c c o u n t . f o r . a n t i . s u p p o r t = TRUE , p e n a l i z e . f o r . h i g h .86d e v i a t i o n = TRUE # T u n i n g p a r a m e t e r s){n a i v e _ avg = mean ( t r [ [ t a r g e t ] ] )n a i v e _med = median ( t r [ [ t a r g e t ] ] )t 0 = Sys . t i m e ( )f o r ( k i n 1 : nrow ( v l ) ) {gt = vl [k , vars ]premises = l i s t ()# ############################################## Mining o f t h e p r e m i s e s f o r t e s t o b j e c t g t :# ############################################for ( i in n . i t e r ) {g . random .
s e t = t r [ sample ( x = 1 : nrow ( t r ) , s i z e = s u b s a m p l e . s i z e ∗nrow ( t r ) ) , c( vars , t a r g e t ) ]# I n t e r s e c t t h e o b j e c t s f r o m random e x t r a c t i o n and a t e s t o b j e c t g t :d e s c 0 = meet . o p e r a t o r ( g t , g . random . s e t [ , − n c o l ( g . random . s e t ) ] ) #−n c o l ( ) t odrop t a r g e t v a r i a b l e# Save a l l t a r g e t v a l u e s f o r h i s t o g r a m :h0 = g .
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