def get_table_dyn(S: pd.Index, n_tot: int, max_length=100):
"""
Parameters
----------
S: pd.Index or np.ndarray
a Series of occurrences
n_tot: int
total number of occurrences in the original events
max_length: int, default=None
maximum number of occurrences for a cycle to cover,
by default it will be set to :math:`\log_{2}\left(|S|\right)`
"""
diffs = np.diff(S)
triples = sliding_window_view(S, 3)
diff_pairs = sliding_window_view(diffs, 2)
dS = S.max() - S.min()
score_one = residual_length(1, n_tot, dS) # 1 really ?
scores = sum(cycle_length(triples, diff_pairs, len(S), dS))
change = scores > 3 * score_one
scores[change] = 3 * score_one # inplace replacement
cut_points = np.array([-1] * len(scores), dtype=object)
cut_points[~change] = None
scores = dict(zip(((i, i + 2) for i in range(len(scores))), scores))
cut_points = dict(zip(scores.keys(), cut_points))
max_length = min([len(S), max_length])
for k in range(4, max_length + 1):
w = sliding_window_view(S, k)
_diffs = sliding_window_view(diffs, k - 1)
_s = sum(cycle_length(w, _diffs, len(S), dS))
for ia, best_score in enumerate(_s):
cut_point = None
iz = ia + k - 1
for im in range(ia, iz):
if im - ia + 1 < 3:
score_left = score_one * (im - ia + 1)
else:
score_left = scores[(ia, im)]
if iz - im < 3:
score_right = score_one * (iz - im)
else:
score_right = scores[(im + 1, iz)]
if score_left + score_right < best_score:
best_score = score_left + score_right
cut_point = im
scores[(ia, iz)] = best_score
cut_points[(ia, iz)] = cut_point
return scores, cut_points
def _check_cutoffs_fh_y(
cutoffs: VALID_CUTOFF_TYPES, fh: FORECASTING_HORIZON_TYPES, y: pd.Index
) -> None:
"""Check that combination of inputs is compatible.
Currently, only two cases are allowed:
either both `cutoffs` and `fh` are integers, or they are datetime or timedelta.
Parameters
----------
cutoffs : np.array or pd.Index
Cutoff points, positive and integer- or datetime-index like.
Type should match the type of `fh` input.
fh : int, timedelta, list or np.ndarray of ints or timedeltas
Type should match the type of `cutoffs` input.
y : pd.Index
Index of time series
Raises
------
ValueError
if max cutoff plus max `fh` is above the last observation in `y`
TypeError
if `cutoffs` and `fh` type combination is not supported
"""
max_cutoff = np.max(cutoffs)
max_fh = fh.max()
msg = "`fh` is incompatible with given `cutoffs` and `y`."
if is_int(x=max_cutoff) and is_int(x=max_fh):
if max_cutoff + max_fh > y.shape[0]:
raise ValueError(msg)
elif is_datetime(x=max_cutoff) and is_timedelta(x=max_fh):
if max_cutoff + max_fh > y.max():
raise ValueError(msg)
else:
raise TypeError("Unsupported type of `cutoffs` and `fh`")
