def test_mode1():
""" Test mode 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, mode(d)[0][0] in [floor((n+1)*p), floor((n+1)*p)-1]
def test_mode2():
""" Test mode on a generic distribution """
d = D([(0, 0), (1, 0), (2, 1), (3, 1)], [1 / 8, 1 / 8, 3 / 8, 3 / 8])
modes = [np.array([2, 3]), np.array([1])]
for m1, m2 in zip(mode(d), modes):
assert np.allclose(m1, m2)
def test_mode1(n, p):
""" Test mode on binomial distribution """
d = binomial(n, p)
assert mode(d)[0][0] in [floor((n + 1) * p), floor((n + 1) * p) - 1]
def test_mode2():
d = D([(0, 0), (1, 0), (2, 1), (3, 1)], [1/8, 1/8, 3/8, 3/8])
modes = [np.array([2, 3]), np.array([1])]
for m1, m2 in zip(mode(d), modes):
assert_array_almost_equal(m1, m2)
def test_mode1():
ns = range(2, 10)
ps = np.linspace(0, 1, 11)
for n, p in product(ns, ps):
d = binomial(n, p)
assert(mode(d)[0][0] in [floor((n+1)*p), floor((n+1)*p)-1])
def test_mode2():
""" Test mode on a generic distribution """
d = D([(0, 0), (1, 0), (2, 1), (3, 1)], [1/8, 1/8, 3/8, 3/8])
modes = [np.array([2, 3]), np.array([1])]
for m1, m2 in zip(mode(d), modes):
assert np.allclose(m1, m2)
def test_mode1(n, p):
""" Test mode on binomial distribution """
d = binomial(n, p)
assert mode(d)[0][0] in [floor((n+1)*p), floor((n+1)*p)-1]
def test_mode2():
""" Test mode on a generic distribution """
d = D([(0, 0), (1, 0), (2, 1), (3, 1)], [1/8, 1/8, 3/8, 3/8])
modes = [np.array([2, 3]), np.array([1])]
for m1, m2 in zip(mode(d), modes):
yield assert_array_almost_equal, m1, m2
