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CvDataSplitter_sample.inp
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CvDataSplitter_sample.inp
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clear
set verbose off
include CvDataSplitter.gfn --force
# EXAMPLE
#=========
open australia.gdt -q
# Define variables
#------------------
series LHS = ldiff(PAU)
list RHS = const LHS(-1 to -2) #ldiff(PUS) IUS(-1 to -2) IAU(-1 to -2)
list L = LHS RHS
# Drop missing values
#---------------------
smpl --no-missing L
# Select method:
#---------------
scalar use_matrices = 0 # 0: pass a list, 1: pass a matrix
# Initialize empty bundle
#-------------------------
bundle b = null
if use_matrices
matrix X = {L}
genr index # when using matrices, you must pass a index vector
matrix mindex = {index}
b.X = X
b.index = mindex
else
list b.X = L
endif
# Select cross-validation type (optional)
#-----------------------------------------
# b.win_size = 6 # optional: window-width for 'recwin'/'rolwin'
# b.n_folds = 3 # optional: divide sample into n_folds of groups for 'kfold'
# b.cv_type = "rolwin" # 'kfold' (default), 'loo', 'recwin', 'rolwin'
# Pass bundle 'b' in pointer form + run
#---------------------------------------
CvDataSplitter(&b)
print b
# Print training + test datasets stored as matrices
#--------------------------------------------------
eval b.X_train[3] # grab array of X for some training set
eval b.X_test[3] # grab array of X for some test set