def naive_idx_to_mp(I, T, m, normalize=True):
I = I.astype(np.int64)
T = T.copy()
T_isfinite = np.isfinite(T)
T_subseqs_isfinite = np.all(core.rolling_window(T_isfinite, m), axis=1)
T[~T_isfinite] = 0.0
T_subseqs = core.rolling_window(T, m)
nn_subseqs = T_subseqs[I]
if normalize:
P = naive.distance(naive.z_norm(T_subseqs, axis=1),
naive.z_norm(nn_subseqs, axis=1),
axis=1)
else:
P = naive.distance(T_subseqs, nn_subseqs, axis=1)
P[~T_subseqs_isfinite] = np.inf
P[I < 0] = np.inf
return P
def test_stumpi_profile_index_match():
T_full = np.random.rand(64)
m = 3
T_full_subseq = core.rolling_window(T_full, m)
warm_start = 8
T_stream = T_full[:warm_start].copy()
stream = stumpi(T_stream, m, egress=True)
P = np.full(stream.P_.shape, np.inf)
left_P = np.full(stream.left_P_.shape, np.inf)
n = 0
for i in range(len(T_stream), len(T_full)):
t = T_full[i]
stream.update(t)
P[:] = np.inf
idx = np.argwhere(stream.I_ >= 0).flatten()
P[idx] = naive.distance(
naive.z_norm(T_full_subseq[idx + n + 1], axis=1),
naive.z_norm(T_full_subseq[stream.I_[idx]], axis=1),
axis=1,
)
left_P[:] = np.inf
idx = np.argwhere(stream.left_I_ >= 0).flatten()
left_P[idx] = naive.distance(
naive.z_norm(T_full_subseq[idx + n + 1], axis=1),
naive.z_norm(T_full_subseq[stream.left_I_[idx]], axis=1),
axis=1,
)
npt.assert_almost_equal(stream.P_, P)
npt.assert_almost_equal(stream.left_P_, left_P)
n += 1