import numpy as np
from sklearn.preprocessing import LabelBinarizer

Y = np.array(['a','b','e','a','e','b','e','a','e'])

lb = LabelBinarizer()
lb.fit(Y)
lb.transform(Y)
nowa_klasa = np.array([0,0,11]).reshape((1,3))

lb.inverse_transform(nowa_klasa)

from tensorflow.keras.utils import to_categorical

YY = np.array([1,2,3,1,2,3])

to_categorical(YY)

X = np.random.sample(60).reshape(30,2)
Y=np.tile([1,2],15) #zwraca nam klasy (1 lub 2) dla każdej kolejnej kolumny (kolumny mamy podane jako 30)


from sklearn.model_selection import train_test_split

X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2 , random_state=0)

from sklearn.model_selection import KFold

kf = KFold(n_splits=5, shuffle=True)

for train, test in kf.split(X):
    X_train = X[train]
    X_test = X[test]
    print("%s " % X_test)

from sklearn.model_selection import LeaveOneOut

loo = LeaveOneOut()

for train, test in loo.split(X):
    print("%s %s" % (train,test))