#NEWLINE# def inverse_transform( self , X , copy = None ) : #NEWLINE# #INDENT# 'Scale back the data to the original representation\n\n        Parameters\n        ----------\n        X : array-like, shape [n_samples, n_features]\n            The data used to scale along the features axis.\n        ' #NEWLINE# check_is_fitted ( self , 'scale_' ) #NEWLINE# copy = ( copy if ( X is not None ) else self . copy ) #NEWLINE# if sparse . issparse ( X ) : #NEWLINE# #INDENT# if self . with_mean : #NEWLINE# #INDENT# raise ValueError ( 'Cannot uncenter sparse matrices: pass `with_mean=False` instead See docstring for motivation and alternatives.' ) #NEWLINE# #UNINDENT# if ( not sparse . isspmatrix_csr ( X ) ) : #NEWLINE# #INDENT# X = X . tocsr ( ) #NEWLINE# copy = False #NEWLINE# #UNINDENT# if copy : #NEWLINE# #INDENT# X = X . copy ( ) #NEWLINE# #UNINDENT# if ( self . scale_ is not None ) : #NEWLINE# #INDENT# inplace_column_scale ( X , self . scale_ ) #NEWLINE# #UNINDENT# #UNINDENT# else : #NEWLINE# #INDENT# X = np . asarray ( X ) #NEWLINE# if copy : #NEWLINE# #INDENT# X = X . copy ( ) #NEWLINE# #UNINDENT# if self . with_std : #NEWLINE# #INDENT# X *= self . scale_ #NEWLINE# #UNINDENT# if self . with_mean : #NEWLINE# #INDENT# X += self . mean_ #NEWLINE# #UNINDENT# #UNINDENT# return X