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_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
