def main():
    print()
    n_actions = 27
    n_subjects = 8
    n_takes = 4
    skeleton_pose_models = ['coco', 'mpi']
    modalities = ['rgb', 'depth', 'inertial']
    model_names = ['gaussian_naive_bayes', 'support_vector_classifier', 'decision_tree_classifier', 'random_forest_classifier', 'extra_trees_classifier', 'gradient_boosting_classifier']
    modality_combinations = list_combinations_generator(modalities)
    train_subject_ids = [i for i in range(1, (n_subjects - 1))]
    validation_subject_ids = [(n_subjects - 1)]
    test_subject_ids = [n_subjects]
    for i in range(len(modality_combinations)):
        for j in range(len(skeleton_pose_models)):
            per_combination_model_training_testing(train_subject_ids, validation_subject_ids, test_subject_ids, n_actions, n_takes, modality_combinations[i], skeleton_pose_models[j], model_names)
            print()
def main():
    print()
    n_actions = 27
    n_subjects = 8
    n_takes = 4
    skeleton_pose_models = ['coco', 'mpi']
    modalities = ['rgb', 'depth', 'inertial']
    model_names = ['gaussian_naive_bayes', 'support_vector_classifier', 'decision_tree_classifier', 'random_forest_classifier', 'extra_trees_classifier', 'gradient_boosting_classifier']
    modality_combinations = list_combinations_generator(modalities)
    train_subject_ids = [i for i in range(1, (n_subjects - 1))]
    validation_subject_ids = [(n_subjects - 1)]
    test_subject_ids = [n_subjects]
    for i in range(len(modality_combinations)):
        for j in range(len(skeleton_pose_models)):
            per_combination_model_training_testing(train_subject_ids, validation_subject_ids, test_subject_ids, n_actions, n_takes, modality_combinations[i], skeleton_pose_models[j], model_names)
            print()