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# HELP: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold

from sklearn.model_selection import KFold
import numpy as np

# data
X = np.array([2.3,4.2,5.3,3.1,4.5,6.2])
y = np.array([34543,24432,85432,32543,75433,21124])

# build object
kf = KFold(n_splits=4, shuffle=False, random_state=None)
# loop of folds
for ii, (train_index, test_index) in enumerate(kf.split(X)):
	print(ii, "indexes of samples for each iteration","TRAIN:", len(train_index), "TEST:", len(test_index))
	# x points of samples (training, test) for each iteration
	X_train, X_test = X[train_index], X[test_index]
	# y points of samples (training, test) for each iteration
	y_train, y_test = y[train_index], y[test_index]
	
	# here include validation for each iteration
	
# here calculate the average error of n_folds iterations