def compute_clusters(X, centers):
    """
    INPUT:
    X - np.array, (n_objects x n_features) - матрица объекты-признаки
    centers - np.array, (n_clusters x n_features) - матрица, содержащая координаты центров кластеров

    OUTPUT:
    y - np.array, (n_objects,) - вектор, состоящий из меток кластеров (0, 1, ..., n_clusters-1), где
    на i-ом месте стоит метка кластера для i-ого объекта выборки X
    """
    #n_objects, n_features = X.shape
    #n_clusters, n_features = centers.shape

    distance = pairwise_distances(X, centers, metric = 'euclidean')
    y = np.argmin(distance, axis = 1)

    return y

centers = (np.arange(6)*0.05).reshape(3, 2)
assert np.allclose(
    compute_clusters(X, centers),
    np.array([0, 2, 2, 2, 2, 0, 1, 2, 2, 2, 1, 0, 0, 0, 0, 2, 0, 2, 0, 0, 2, 0,
       2, 0, 2, 0, 0, 2, 2, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0,
       0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0,
       2, 0, 0, 1, 2, 0, 0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0,
       2, 1, 0, 0, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))