recall += metric.recall()
matrices += [metric.confusion_matrix()]
f1 /= n_folds
precision /= n_folds
recall /= n_folds
matrices = np.array(matrices)
return f1, precision, recall, matrices
if __name__ == '__main__':
print('Q-Learning')
# MIT1 has not overlapping activities
path = DatasetPath.MIT2
dp = DataProcessor(path=path)
dp.data_processed = Parser().data()
ql = QLearning(dp)
ql.fit(dp.data_processed)
f1, precision, recall, matrices = ql.evaluate()
print(f'F1 = {f1}')
print(f'Precision = {precision}')
print(f'Recall = {recall}')
