def select(features, labels, R): from milk.utils.utils import get_nprandom R = get_nprandom(R) ss = [] for ells in labels.T: active = ~np.isnan(ells) ss.append(select1(features[active,:], ells[active], R)) ss = np.array(ss) for cutoff in xrange(20,0): selected = (ss.sum(0) > cutoff) if np.sum(selected) > 10: return selected return np.ones(features.shape[1], bool)
def test_nprandom(): assert get_nprandom(None).rand() != get_nprandom(None).rand() assert get_nprandom(1).rand() != get_nprandom(2).rand() assert get_nprandom(1).rand() == get_nprandom(1).rand() r = get_nprandom(1) assert get_nprandom(r).rand() != r.rand()
def test_cross_random(): assert get_pyrandom(get_nprandom(1)).random() == get_pyrandom(get_nprandom(1)).random() assert get_nprandom(get_pyrandom(1)).rand() == get_nprandom(get_pyrandom(1)).rand()
def test_cross_random(): assert get_pyrandom(get_nprandom(1)).random() == get_pyrandom( get_nprandom(1)).random() assert get_nprandom(get_pyrandom(1)).rand() == get_nprandom( get_pyrandom(1)).rand()
def __init__(self, seed): from milk.utils.utils import get_nprandom self.R = get_nprandom(seed)