def test_stimp(T, m):
if T.ndim > 1:
T = T.copy()
T = T[0]
n = 3
seed = np.random.randint(100000)
np.random.seed(seed)
ref = stumpy.aamp_stimp(T, m)
for i in range(n):
ref.update()
np.random.seed(seed)
cmp = stumpy.stimp(T, m, normalize=False)
for i in range(n):
cmp.update()
# Compare raw pan
ref_PAN = ref._PAN
cmp_PAN = cmp._PAN
naive.replace_inf(ref_PAN)
naive.replace_inf(cmp_PAN)
npt.assert_almost_equal(ref_PAN, cmp_PAN)
# Compare transformed pan
npt.assert_almost_equal(ref.PAN_, cmp.PAN_)
def test_stimp_max_m(T):
threshold = 0.2
percentage = 0.01
min_m = 3
max_m = 5
n = T.shape[0] - min_m + 1
seed = np.random.randint(100000)
np.random.seed(seed)
pan = stimp(
T,
min_m=min_m,
max_m=max_m,
step=1,
percentage=percentage,
pre_scrump=True,
# normalize=True,
)
for i in range(n):
pan.update()
ref_PAN = np.full((pan.M_.shape[0], T.shape[0]), fill_value=np.inf)
np.random.seed(seed)
for idx, m in enumerate(pan.M_[:n]):
zone = int(np.ceil(m / 4))
s = zone
tmp_P, tmp_I = naive.prescrump(T, m, T, s=s, exclusion_zone=zone)
ref_mp = naive.scrump(T, m, T, percentage, zone, True, s)
for i in range(ref_mp.shape[0]):
if tmp_P[i] < ref_mp[i, 0]:
ref_mp[i, 0] = tmp_P[i]
ref_mp[i, 1] = tmp_I[i]
ref_PAN[pan._bfs_indices[idx], :ref_mp.shape[0]] = ref_mp[:, 0]
# Compare raw pan
cmp_PAN = pan._PAN
naive.replace_inf(ref_PAN)
naive.replace_inf(cmp_PAN)
npt.assert_almost_equal(ref_PAN, cmp_PAN)
# Compare transformed pan
cmp_pan = pan.PAN_
ref_pan = naive.transform_pan(pan._PAN, pan._M, threshold,
pan._bfs_indices, pan._n_processed)
naive.replace_inf(ref_pan)
naive.replace_inf(cmp_pan)
npt.assert_almost_equal(ref_pan, cmp_pan)
def test_stimp_max_m(T):
percentage = 0.01
min_m = 3
max_m = 5
n = T.shape[0] - min_m + 1
seed = np.random.randint(100000)
np.random.seed(seed)
pmp = stimp(
T,
min_m=min_m,
max_m=max_m,
step=1,
percentage=percentage,
pre_scrump=True,
normalize=True,
)
for i in range(n):
pmp.update()
ref_P = np.full((pmp.M_.shape[0], T.shape[0]), fill_value=np.inf)
ref_I = np.ones((pmp.M_.shape[0], T.shape[0]), dtype=np.int64) * -1
np.random.seed(seed)
for idx, m in enumerate(pmp.M_[:n]):
zone = int(np.ceil(m / 4))
s = zone
tmp_P, tmp_I = naive.prescrump(T, m, T, s=s, exclusion_zone=zone)
ref_mp = naive.scrump(T, m, T, percentage, zone, True, s)
for i in range(ref_mp.shape[0]):
if tmp_P[i] < ref_mp[i, 0]:
ref_mp[i, 0] = tmp_P[i]
ref_mp[i, 1] = tmp_I[i]
ref_P[pmp.bfs_indices_[idx], :ref_mp.shape[0]] = ref_mp[:, 0]
ref_I[pmp.bfs_indices_[idx], :ref_mp.shape[0]] = ref_mp[:, 1]
comp_P = pmp.P_
comp_I = pmp.I_
naive.replace_inf(ref_P)
naive.replace_inf(ref_I)
naive.replace_inf(comp_P)
naive.replace_inf(comp_I)
npt.assert_almost_equal(ref_P, comp_P)
npt.assert_almost_equal(ref_I, comp_I)
def test_stimp_100_percent(T):
threshold = 0.2
percentage = 1.0
min_m = 3
n = T.shape[0] - min_m + 1
pan = stimp(
T,
min_m=min_m,
max_m=None,
step=1,
percentage=percentage,
pre_scrump=True,
# normalize=True,
)
for i in range(n):
pan.update()
ref_PAN = np.full((pan.M_.shape[0], T.shape[0]), fill_value=np.inf)
for idx, m in enumerate(pan.M_[:n]):
zone = int(np.ceil(m / 4))
ref_mp = naive.stump(T, m, T_B=None, exclusion_zone=zone)
ref_PAN[pan._bfs_indices[idx], :ref_mp.shape[0]] = ref_mp[:, 0]
# Compare raw pan
cmp_PAN = pan._PAN
naive.replace_inf(ref_PAN)
naive.replace_inf(cmp_PAN)
npt.assert_almost_equal(ref_PAN, cmp_PAN)
# Compare transformed pan
cmp_pan = pan.PAN_
ref_pan = naive.transform_pan(pan._PAN, pan._M, threshold,
pan._bfs_indices, pan._n_processed)
naive.replace_inf(ref_pan)
naive.replace_inf(cmp_pan)
npt.assert_almost_equal(ref_pan, cmp_pan)
