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_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 __init__(self, seed):
from milk.utils.utils import get_nprandom
self.R = get_nprandom(seed)
