def test_cac(I):
L = 5
excl_factor = 1
custom_iac = _iac(I.shape[0])
ref = naive_cac(I, L, excl_factor, custom_iac)
bidirectional = True
comp = _cac(I, L, bidirectional, excl_factor)
npt.assert_almost_equal(ref, comp)
def test_cac(I):
L = 5
excl_factor = 1
custom_iac = _iac(I.shape[0])
left = naive_cac(I, L, excl_factor, custom_iac)
bidirectional = True
right = _cac(I, L, bidirectional, excl_factor)
npt.assert_almost_equal(left, right)
def test_floss():
data = np.random.uniform(-1000, 1000, [64])
m = 5
old_data = data[:30]
n = old_data.shape[0]
add_data = data[30:]
mp = naive_right_mp(old_data, m)
comp_mp = stump(old_data, m)
k = mp.shape[0]
rolling_Ts = core.rolling_window(data[1:], n)
L = 5
excl_factor = 1
custom_iac = _iac(k, bidirectional=False)
stream = floss(comp_mp, old_data, m, L, excl_factor, custom_iac=custom_iac)
last_idx = n - m + 1
excl_zone = int(np.ceil(m / 4))
zone_start = max(0, k - excl_zone)
for i, ref_T in enumerate(rolling_Ts):
mp[:, 1] = -1
mp[:, 2] = -1
mp[:] = np.roll(mp, -1, axis=0)
mp[-1, 0] = np.inf
mp[-1, 3] = last_idx + i
D = naive_distance_profile(ref_T[-m:], ref_T, m)
D[zone_start:] = np.inf
update_idx = np.argwhere(D < mp[:, 0]).flatten()
mp[update_idx, 0] = D[update_idx]
mp[update_idx, 3] = last_idx + i
ref_cac_1d = _cac(
mp[:, 3] - i - 1,
L,
bidirectional=False,
excl_factor=excl_factor,
custom_iac=custom_iac,
)
ref_mp = mp.copy()
ref_P = ref_mp[:, 0]
ref_I = ref_mp[:, 3]
stream.update(ref_T[-1])
comp_cac_1d = stream.cac_1d_
comp_P = stream.P_
comp_I = stream.I_
comp_T = stream.T_
naive.replace_inf(ref_P)
naive.replace_inf(comp_P)
npt.assert_almost_equal(ref_cac_1d, comp_cac_1d)
npt.assert_almost_equal(ref_P, comp_P)
npt.assert_almost_equal(ref_I, comp_I)
npt.assert_almost_equal(ref_T, comp_T)
def test_floss():
data = np.random.uniform(-1000, 1000, [64])
m = 5
old_data = data[:30]
n = old_data.shape[0]
add_data = data[30:]
left_mp = naive_right_mp(old_data, m)
right_mp = stump(old_data, m)
k = left_mp.shape[0]
rolling_Ts = core.rolling_window(data[1:], n)
L = 5
excl_factor = 1
custom_iac = _iac(k, bidirectional=False)
right_gen = floss(
right_mp, old_data, add_data, m, L, excl_factor, custom_iac=custom_iac
)
last_idx = n - m + 1
excl_zone = int(np.ceil(m / 4))
zone_start = max(0, k - excl_zone)
for i, T in enumerate(rolling_Ts):
left_mp[:] = np.roll(left_mp, -1, axis=0)
left_mp[-1, 0] = np.inf
left_mp[-1, 3] = last_idx + i
D = naive_distance_profile(T[-m:], T, m)
D[zone_start:] = np.inf
update_idx = np.argwhere(D < left_mp[:, 0]).flatten()
left_mp[update_idx, 0] = D[update_idx]
left_mp[update_idx, 3] = last_idx + i
left_cac = _cac(
left_mp[:, 3] - i - 1,
L,
bidirectional=False,
excl_factor=excl_factor,
custom_iac=custom_iac,
)
right_cac, right_mp, tmp_T = next(right_gen)
npt.assert_almost_equal(left_cac, right_cac)