def test_median1():
""" Test median on binomial distribution """
ns = range(2, 10)
ps = np.linspace(0, 1, 11)
for n, p in product(ns, ps):
d = binomial(n, p)
yield assert_true, median(d) in [floor(n*p), n*p, ceil(n*p)]
def test_median2():
""" Test median on a generic distribution """
d = D([(0, 0), (1, 0), (2, 1), (3, 1)], [1 / 8, 1 / 8, 3 / 8, 3 / 8])
assert np.allclose(median(d), [2, 1])
def test_median1(n, p):
""" Test median on binomial distribution """
d = binomial(n, p)
assert median(d) in [floor(n * p), n * p, ceil(n * p)]
def test_median2():
d = D([(0, 0), (1, 0), (2, 1), (3, 1)], [1/8, 1/8, 3/8, 3/8])
assert_array_almost_equal(median(d), [2, 1])
def test_median1():
ns = range(2, 10)
ps = np.linspace(0, 1, 11)
for n, p in product(ns, ps):
d = binomial(n, p)
assert(median(d) in [floor(n*p), n*p, ceil(n*p)])
def test_median2():
""" Test median on a generic distribution """
d = D([(0, 0), (1, 0), (2, 1), (3, 1)], [1/8, 1/8, 3/8, 3/8])
assert np.allclose(median(d), [2, 1])
def test_median1(n, p):
""" Test median on binomial distribution """
d = binomial(n, p)
assert median(d) in [floor(n*p), n*p, ceil(n*p)]
def test_median2():
""" Test median on a generic distribution """
d = D([(0, 0), (1, 0), (2, 1), (3, 1)], [1/8, 1/8, 3/8, 3/8])
assert_array_almost_equal(median(d), [2, 1])
