[VI] Cryotherapy

#Naiven Baes so neprekinati domeni
import os
os.environ['OPENBLAS_NUM_THREADS'] = '1'
from sklearn.preprocessing import OrdinalEncoder
from sklearn.naive_bayes import GaussianNB

if __name__ == '__main__':

    dataset = [['1', '35', '12', '5', '1', '100', '0'], ['1', '29', '7', '5', '1', '96', '1'],
               ['1', '50', '8', '1', '3', '132', '0'], ['1', '32', '11.75', '7', '3', '750', '0'],
               ['1', '67', '9.25', '1', '1', '42', '0'], ['1', '41', '8', '2', '2', '20', '1'],
               ['1', '19', '8', '8', '1', '160', '1']]

    dataset_float=[]
    for row in dataset:
        row_df= [float(el) for el in row]
        dataset_float.append(row_df)


    train_set= dataset_float[:int(0.85*len(dataset_float))]
    train_x= [row[:-1] for row in train_set]
    train_y= [row[-1] for row in train_set]

    test_set = dataset_float[int(0.85 * len(dataset_float)):]
    test_x= [row[:-1] for row in test_set]
    test_y= [row[-1] for row in test_set]

    classifier = GaussianNB()
    classifier.fit(train_x, train_y)

    accuracy=0
    for i in range(len(test_set)):
        if classifier.predict([test_x[i]])[0] == test_y[i]:
            accuracy+=1

    print(accuracy/len(test_set))
    entry=[float(el) for el in input().split(' ')]

    print(int(classifier.predict([entry])[0]))
    print(classifier.predict_proba([entry]))