def test_preprocess():
T = np.array([0, np.nan, 2, 3, 4, 5, 6, 7, np.inf, 9])
m = 3
ref_T = np.array([0, 0, 2, 3, 4, 5, 6, 7, 0, 9], dtype=float)
ref_M, ref_Σ = naive_compute_mean_std(T, m)
comp_T, comp_M, comp_Σ = core.preprocess(T, m)
npt.assert_almost_equal(ref_T, comp_T)
npt.assert_almost_equal(ref_M, comp_M)
npt.assert_almost_equal(ref_Σ, comp_Σ)
T = pd.Series(T)
comp_T, comp_M, comp_Σ = core.preprocess(T, m)
npt.assert_almost_equal(ref_T, comp_T)
npt.assert_almost_equal(ref_M, comp_M)
npt.assert_almost_equal(ref_Σ, comp_Σ)
def test_preprocess():
T = np.array([0, np.nan, 2, 3, 4, 5, 6, 7, np.inf, 9])
m = 3
left_T = np.array([0, 0, 2, 3, 4, 5, 6, 7, 0, 9], dtype=float)
left_M, left_Σ = naive_compute_mean_std(T, m)
right_T, right_M, right_Σ = core.preprocess(T, m)
npt.assert_almost_equal(left_T, right_T)
npt.assert_almost_equal(left_M, right_M)
npt.assert_almost_equal(left_Σ, right_Σ)
T = pd.Series(T)
right_T, right_M, right_Σ = core.preprocess(T, m)
npt.assert_almost_equal(left_T, right_T)
npt.assert_almost_equal(left_M, right_M)
npt.assert_almost_equal(left_Σ, right_Σ)
def __init__(self, T, m, excl_zone=None):
"""
Initialize the `stumpi` object
Parameters
----------
T : ndarray
The time series or sequence for which the matrix profile and matrix profile
indices will be returned
m : int
Window size
excl_zone : int
The half width for the exclusion zone relative to the current
sliding window
"""
self._T = T
self._m = m
if excl_zone is not None: # pragma: no cover
self._excl_zone = excl_zone
else:
self._excl_zone = int(np.ceil(self._m / 4))
self._illegal = np.logical_or(np.isinf(self._T), np.isnan(self._T))
mp = stumpy.stump(self._T, self._m)
self._P = mp[:, 0]
self._I = mp[:, 1]
self._left_I = mp[:, 2]
self._left_P = np.empty(self._P.shape)
self._left_P[:] = np.inf
self._T, self._M_T, self._Σ_T = core.preprocess(self._T, self._m)
# Retrieve the left matrix profile values
for i, j in enumerate(self._left_I):
if j >= 0:
D = core.mass(self._T[i:i + self._m], self._T[j:j + self._m])
self._left_P[i] = D[0]
Q = self._T[-m:]
self._QT = core.sliding_dot_product(Q, self._T)
