def test_em():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
alpha_zh = .1
beta_zs = .1
assert (Theta_zh == 0).all()
assert (Psi_sz == 0).all()
old_Count_zh = Count_zh.copy()
old_Count_sz = Count_sz.copy()
old_count_h = count_h.copy()
old_count_z = count_z.copy()
_learn.em(tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, prob_topics_aux, Theta_zh, Psi_sz, \
10, 2, kernel)
assert (Theta_zh > 0).sum() > 0
assert (Psi_sz > 0).sum() > 0
assert_almost_equal(1, Theta_zh.sum(axis=0))
assert_almost_equal(1, Psi_sz.sum(axis=0))
assert (old_Count_zh != Count_zh).any()
assert (old_Count_sz != Count_sz).any()
assert (old_count_h == count_h).all() #the count_h should not change
def test_full_learn_null():
kernel = NoopKernel()
kernel.build(1, 20, np.zeros(0, dtype='d'))
rv = learn.fit(files.SIZE1K, 20, .1, .1, kernel, \
np.zeros(0, dtype='d'), 1000, 200)
Count_zh = rv['Count_zh']
Count_sz = rv['Count_sz']
assert_equal(Count_zh.sum(), 1000)
assert_equal(Count_sz.sum(), 2000)
count_h = rv['count_h']
count_z = rv['count_z']
assert_equal(count_h.sum(), 1000)
assert_equal(count_z.sum(), 2000)
assert rv['assign'].shape == (1000, )
Theta_zh = rv['Theta_zh']
Psi_sz = rv['Psi_sz']
assert_almost_equal(1, Theta_zh.sum(axis=0))
assert_almost_equal(1, Psi_sz.sum(axis=0))
def test_estep():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
alpha_zh = .1
beta_zs = .1
assert_equal(Count_zh.sum(), 10)
assert_equal(Count_sz.sum(), 20)
assert_equal(count_h[0], 4)
assert_equal(count_h[1], 4)
assert_equal(count_h[2], 2)
new_state = _learn._e_step(tstamps, Trace, previous_stamps, Count_zh, \
Count_sz, count_h, count_z, alpha_zh, beta_zs, prob_topics_aux, \
kernel)
assert_equal(count_h[0], 4)
assert_equal(count_h[1], 4)
assert_equal(count_h[2], 2)
assert_equal(Count_zh.sum(), 10)
assert_equal(Count_sz.sum(), 20)
def test_em():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
alpha_zh = .1
beta_zs = .1
assert (Theta_zh == 0).all()
assert (Psi_sz == 0).all()
old_Count_zh = Count_zh.copy()
old_Count_sz = Count_sz.copy()
old_count_h = count_h.copy()
old_count_z = count_z.copy()
_learn.em(tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, prob_topics_aux, Theta_zh, Psi_sz, \
10, 2, kernel)
assert (Theta_zh > 0).sum() > 0
assert (Psi_sz > 0).sum() > 0
assert_almost_equal(1, Theta_zh.sum(axis=0))
assert_almost_equal(1, Psi_sz.sum(axis=0))
assert (old_Count_zh != Count_zh).any()
assert (old_Count_sz != Count_sz).any()
assert (old_count_h == count_h).all() #the count_h should not change
def test_estep():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
alpha_zh = .1
beta_zs = .1
assert_equal(Count_zh.sum(), 10)
assert_equal(Count_sz.sum(), 20)
assert_equal(count_h[0], 4)
assert_equal(count_h[1], 4)
assert_equal(count_h[2], 2)
new_state = _learn._e_step(tstamps, Trace, previous_stamps, Count_zh, \
Count_sz, count_h, count_z, alpha_zh, beta_zs, prob_topics_aux, \
kernel)
assert_equal(count_h[0], 4)
assert_equal(count_h[1], 4)
assert_equal(count_h[2], 2)
assert_equal(Count_zh.sum(), 10)
assert_equal(Count_sz.sum(), 20)
def test_sample():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
tstamp_idx = 3
hyper = Trace[tstamp_idx, 0]
source = Trace[tstamp_idx, 1]
dest = Trace[tstamp_idx, 2]
old_topic = Trace[tstamp_idx, 3]
new_topic = _learn._sample(tstamp_idx, tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, .1, .1, prob_topics_aux, kernel)
assert new_topic <= 3
def test_sample():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
tstamp_idx = 3
hyper = Trace[tstamp_idx, 0]
source = Trace[tstamp_idx, 1]
dest = Trace[tstamp_idx, 2]
old_topic = Trace[tstamp_idx, 3]
new_topic = _learn._sample(tstamp_idx, tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, .1, .1, prob_topics_aux, kernel)
assert new_topic <= 3
