def test_m_step():
# make sure that the lower bound increases
ips = init_ips(M, K, alpha, docs)
alpha_ = alpha
old_lb_val = lower_bound(ips, phi, alpha, beta, docs, V)
alpha_, beta_ = m_step(ips, phi, alpha, docs, V)
new_lb_val = lower_bound(ips, phi, alpha_, beta_, docs, V)
assert_true(new_lb_val >= old_lb_val)
def test_e_step():
old_ips = init_ips(M, K, alpha, docs)
new_ips, phi = e_step(alpha, beta, docs)
# very permissive test(not sure how to test it better):
# make sure new_ips changes
assert_true(np.abs(new_ips - old_ips).min() >= 1e-5)
# make sure sum to one
for m in xrange(phi.size):
assert_array_almost_equal(phi[m].sum(axis=1), 1)
# make sure the lower bound increases
old_lb_val = lower_bound(old_ips, phi, alpha, beta, docs, V)
new_ips, new_phi = e_step(alpha, beta, docs)
new_lb_val = lower_bound(new_ips, new_phi, alpha, beta, docs, V)
assert_true(new_lb_val >= old_lb_val)
def test_lower_bound():
ips = init_ips(M, K, alpha, docs)
actual = lower_bound(ips, phi, alpha, beta, docs, V)
# how to test if we don't want to calculate by hand
# 1st, <= 0
assert_true(actual <= 0)
# 2nd, after training several iterations
# the value should be increasing
# as our goal is to maximize the lower bound
old_lb_val = actual
alpha_, beta_ = alpha, beta
ips_, phi_ = ips, phi
for i in xrange(10):
ips_, phi_ = e_step(alpha_, beta_, docs)
alpha_, beta_ = m_step(ips_, phi_, alpha_, docs, V)
new_lb_val = lower_bound(ips_, phi_, alpha_, beta_, docs, V)
assert_true(new_lb_val >= old_lb_val)
old_lb_val = new_lb_val