def test_correlate_all():
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)
C = dynamic.correlate_counts(Count_zh, Count_sz, count_h, count_z, .1, \
.1)
assert_equal((2, 2), C.shape)
assert C[0, 1] != 0
assert (np.tril(C) == 0).all()
def test_correlate_all():
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)
C = dynamic.correlate_counts(Count_zh, Count_sz, count_h, count_z, .1, \
.1)
assert_equal((2, 2), C.shape)
assert C[0, 1] != 0
assert (np.tril(C) == 0).all()
def test_merge():
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 = ECCDFKernel()
kernel.build(Trace.shape[0], Count_zh.shape[0], \
np.array([1.0, Count_zh.shape[0] - 1]))
Trace[:, 0] = 0
Trace[:, 1] = 0
Trace[:, 2] = 0
Trace[:, 3] = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
Count_sz[:] = 0
Count_zh[:] = 0
count_z[:] = 0
count_h[:] = 0
fast_populate(Trace, Count_zh, Count_sz, count_h, count_z)
C = dynamic.correlate_counts(Count_zh, Count_sz, count_h, count_z, .1, \
.1)
alpha_zh, beta_zs, beta_zd = [0.1] * 3
a_ptz = 1.0
b_ptz = Count_zh.shape[0] - 1
ll_per_z = np.zeros(2, dtype='f8')
quality_estimate(tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zd, ll_per_z, \
np.arange(Trace.shape[0], dtype='i4'), kernel)
Trace_new, Count_zh_new, Count_sz_new, \
count_z_new, new_stamps, _ = \
dynamic.merge(tstamps, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, alpha_zh, beta_zs, ll_per_z, kernel)
print(Trace_new)
print(np.array(new_stamps._get_all(0)))
assert len(new_stamps._get_all(0)) == 10
assert Count_zh_new.shape[0] < Count_zh.shape[0]
assert Count_zh_new.shape[1] == Count_zh.shape[1]
assert Count_sz_new.shape[0] == Count_sz.shape[0]
assert Count_sz_new.shape[1] < Count_sz.shape[0]
assert count_z_new.shape[0] < count_z.shape[0]
def test_merge():
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 = ECCDFKernel()
kernel.build(Trace.shape[0], Count_zh.shape[0], \
np.array([1.0, Count_zh.shape[0] - 1]))
Trace[:, 0] = 0
Trace[:, 1] = 0
Trace[:, 2] = 0
Trace[:, 3] = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
Count_sz[:] = 0
Count_zh[:] = 0
count_z[:] = 0
count_h[:] = 0
fast_populate(Trace, Count_zh, Count_sz, count_h, count_z)
C = dynamic.correlate_counts(Count_zh, Count_sz, count_h, count_z, .1, \
.1)
alpha_zh, beta_zs, beta_zd = [0.1] * 3
a_ptz = 1.0
b_ptz = Count_zh.shape[0] - 1
ll_per_z = np.zeros(2, dtype='f8')
quality_estimate(tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zd, ll_per_z, \
np.arange(Trace.shape[0], dtype='i4'), kernel)
Trace_new, Count_zh_new, Count_sz_new, \
count_z_new, new_stamps, _ = \
dynamic.merge(tstamps, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, alpha_zh, beta_zs, ll_per_z, kernel)
print(Trace_new)
print(np.array(new_stamps._get_all(0)))
assert len(new_stamps._get_all(0)) == 10
assert Count_zh_new.shape[0] < Count_zh.shape[0]
assert Count_zh_new.shape[1] == Count_zh.shape[1]
assert Count_sz_new.shape[0] == Count_sz.shape[0]
assert Count_sz_new.shape[1] < Count_sz.shape[0]
assert count_z_new.shape[0] < count_z.shape[0]
