[VI] Solar Beam

#Naiven Baes so Kategoricki domeni
import os
os.environ['OPENBLAS_NUM_THREADS'] = '1'
from sklearn.naive_bayes import CategoricalNB
from sklearn.preprocessing import OrdinalEncoder
import warnings
warnings.filterwarnings("ignore")

if __name__ == '__main__':
    dataset = [['C', 'S', 'O', '1', '2', '1', '1', '2', '1', '2', '0'],
               ['D', 'S', 'O', '1', '3', '1', '1', '2', '1', '2', '0'],
               ['C', 'S', 'O', '1', '3', '1', '1', '2', '1', '1', '0'],
               ['D', 'S', 'O', '1', '3', '1', '1', '2', '1', '2', '0'],
               ['D', 'A', 'O', '1', '3', '1', '1', '2', '1', '2', '0'],
               ['D', 'A', 'O', '1', '2', '1', '1', '2', '1', '2', '0']]

    encoder= OrdinalEncoder()
    encoder.fit([row[:-1] for row in dataset])

    train_set= dataset[:int(0.75*len(dataset))]
    train_set_x= [row[:-1] for row in train_set]
    train_set_y= [row[-1:] for row in train_set]
    train_set_x= encoder.transform(train_set_x)

    test_set= dataset[int(0.75*len(dataset)):]
    test_set_x= [row[:-1] for row in test_set]
    test_set_y= [row[-1:] for row in test_set]
    test_set_x = encoder.transform(test_set_x)

    classifier= CategoricalNB()
    classifier.fit(train_set_x,train_set_y)

    accuracy=0
    for i in range(len(test_set)):
        predicted_class= classifier.predict([test_set_x[i]])[0]
        real_class= test_set_y[i][0]
        if predicted_class == real_class:
            accuracy+=1

    print(accuracy/len(test_set_x))

    entry = [el for el in input().split(' ')]
    encoded_entry= encoder.transform([entry])

    print(classifier.predict(encoded_entry)[0])
    print(classifier.predict_proba(encoded_entry))