def test_simple_values():
alpha = np.array([1, 1])
d = dirichlet(alpha)
assert_almost_equal(d.mean(), 0.5)
assert_almost_equal(d.var(), 1. / 12.)
b = beta(1, 1)
assert_almost_equal(d.mean(), b.mean())
assert_almost_equal(d.var(), b.var())
def test_simple_values():
alpha = np.array([1, 1])
d = dirichlet(alpha)
assert_almost_equal(d.mean(), 0.5)
assert_almost_equal(d.var(), 1. / 12.)
b = beta(1, 1)
assert_almost_equal(d.mean(), b.mean())
assert_almost_equal(d.var(), b.var())
def test_2D_dirichlet_is_beta():
np.random.seed(2846)
alpha = np.random.uniform(10e-10, 100, 2)
d = dirichlet(alpha)
b = beta(alpha[0], alpha[1])
num_tests = 10
for i in range(num_tests):
x = np.random.uniform(10e-10, 100, 2)
x /= np.sum(x)
assert_almost_equal(b.pdf(x), d.pdf([x]))
assert_almost_equal(b.mean(), d.mean()[0])
assert_almost_equal(b.var(), d.var()[0])
def test_2D_dirichlet_is_beta():
np.random.seed(2846)
alpha = np.random.uniform(10e-10, 100, 2)
d = dirichlet(alpha)
b = beta(alpha[0], alpha[1])
num_tests = 10
for i in range(num_tests):
x = np.random.uniform(10e-10, 100, 2)
x /= np.sum(x)
assert_almost_equal(b.pdf(x), d.pdf([x]))
assert_almost_equal(b.mean(), d.mean()[0])
assert_almost_equal(b.var(), d.var()[0])
