def test_update_alpha():
ips = init_ips(M, K, alpha, docs)
new_alpha = update_alpha(M, ips, alpha)
# gradient should be close to zeros after convergence
assert_array_almost_equal(gradient_g(M, new_alpha, ips),
np.zeros(new_alpha.shape))
def test_gradiant_g():
ips = init_ips(M, K, alpha, docs)
g = gradient_g(M, alpha, ips)
for i in xrange(K):
expected = (M * (psi(alpha.sum()) - psi(alpha[i])) +
np.sum([(psi(ips[d, i]) -
psi(ips[d, :].sum()))
for d in xrange(M)]))
actual = g[i]
assert_array_almost_equal(expected, actual)
