def test_all():
slists = StampLists(4)
E = []
for i in xrange(4):
E.append([])
for _ in xrange(200):
e = np.random.rand()
slists._append(i, e)
E[i].append(e)
kern = ECCDFKernel(True)
kern.build(800, 4, np.array([1.0, 3.0]))
kern._mstep(slists)
for i in xrange(4):
assert_array_equal(sorted(E[i]), slists._get_all(i))
for i in xrange(4):
for _ in xrange(200):
p = kern._pdf(i, np.random.rand(), slists)
assert p <= 1
assert p >= 0
def test_split():
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)
alpha_zh, beta_zs, a_ptz, b_ptz = [0.1] * 4
ll_per_z = np.zeros(2, dtype='f8')
Trace[:, -1] = 0
previous_stamps._clear()
tstamps = np.array([[1.0, 2.0, 3.0, 4.0, 5.0, 100, 200, 300, 400, 500]]).T
tstamps = np.array(tstamps, order='C')
previous_stamps._extend(0, tstamps[:, 0])
Count_zh = np.zeros(shape=(1, Count_zh.shape[1]), dtype='i4')
Count_sz = np.zeros(shape=(Count_sz.shape[0], 1), dtype='i4')
count_z = np.zeros(shape=(1, ), dtype='i4')
fast_populate(Trace, Count_zh, Count_sz, count_h, count_z)
kernel = ECCDFKernel()
kernel.build(Trace.shape[0], Count_zh.shape[0], \
np.array([1.0, Count_zh.shape[0] - 1]))
ll_per_z = np.zeros(1, dtype='f8')
quality_estimate(tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, \
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.split(tstamps, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, alpha_zh, beta_zs, \
ll_per_z, kernel, .5, 0)
assert_array_equal(Trace_new[:, -1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
assert_array_equal(new_stamps._get_all(0), [1, 2, 3, 4, 5])
assert_array_equal(new_stamps._get_all(1), [100, 200, 300, 400, 500])
assert Count_zh_new.shape[0] > Count_zh.shape[0]
assert Count_zh_new.shape[1] == Count_zh.shape[1]
assert Count_zh_new[0].sum() == 5
assert Count_zh_new[1].sum() == 5
assert Count_sz_new.shape[0] == Count_sz.shape[0]
assert Count_sz_new.shape[1] > Count_sz.shape[1]
assert Count_sz_new[:, 0].sum() == 10
assert Count_sz_new[:, 1].sum() == 10
assert count_z_new.shape[0] > count_z.shape[0]
assert count_z_new[0] == 10
assert count_z_new[1] == 10
def test_split():
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)
alpha_zh, beta_zs, a_ptz, b_ptz = [0.1] * 4
ll_per_z = np.zeros(2, dtype='f8')
Trace[:, -1] = 0
previous_stamps._clear()
tstamps = np.array([[1.0, 2.0, 3.0, 4.0, 5.0, 100, 200, 300, 400, 500]]).T
tstamps = np.array(tstamps, order='C')
previous_stamps._extend(0, tstamps[:, 0])
Count_zh = np.zeros(shape=(1, Count_zh.shape[1]), dtype='i4')
Count_sz = np.zeros(shape=(Count_sz.shape[0], 1), dtype='i4')
count_z = np.zeros(shape=(1, ), dtype='i4')
fast_populate(Trace, Count_zh, Count_sz, count_h, count_z)
kernel = ECCDFKernel()
kernel.build(Trace.shape[0], Count_zh.shape[0], \
np.array([1.0, Count_zh.shape[0] - 1]))
ll_per_z = np.zeros(1, dtype='f8')
quality_estimate(tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, \
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.split(tstamps, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, alpha_zh, beta_zs, \
ll_per_z, kernel, .5, 0)
assert_array_equal(Trace_new[:, -1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
assert_array_equal(new_stamps._get_all(0), [1, 2, 3, 4, 5])
assert_array_equal(new_stamps._get_all(1), [100, 200, 300, 400, 500])
assert Count_zh_new.shape[0] > Count_zh.shape[0]
assert Count_zh_new.shape[1] == Count_zh.shape[1]
assert Count_zh_new[0].sum() == 5
assert Count_zh_new[1].sum() == 5
assert Count_sz_new.shape[0] == Count_sz.shape[0]
assert Count_sz_new.shape[1] > Count_sz.shape[1]
assert Count_sz_new[:, 0].sum() == 10
assert Count_sz_new[:, 1].sum() == 10
assert count_z_new.shape[0] > count_z.shape[0]
assert count_z_new[0] == 10
assert count_z_new[1] == 10
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]