# STUMPY
# Copyright 2019 TD Ameritrade. Released under the terms of the 3-Clause BSD license. # noqa: E501
# STUMPY is a trademark of TD Ameritrade IP Company, Inc. All rights reserved.
from collections import deque
import numpy as np
[docs]
def atsc(IL, IR, j):
"""
Compute the anchored time series chain (ATSC)
Note that since the matrix profile indices, ``IL`` and ``IR``, are pre-computed,
this function is agnostic to subsequence normalization.
Parameters
----------
IL : numpy.ndarray
Left matrix profile indices.
IR : numpy.ndarray
Right matrix profile indices.
j : int
The index value for which to compute the ATSC.
Returns
-------
out : numpy.ndarray
Anchored time series chain for index, ``j``
See Also
--------
stumpy.allc : Compute the all-chain set (ALLC)
Notes
-----
`DOI: 10.1109/ICDM.2017.79 <https://www.cs.ucr.edu/~eamonn/chains_ICDM.pdf>`__
See Table I
This is the implementation for the anchored time series chains (ATSC).
Unlike the original paper, we've replaced the while-loop with a more stable
for-loop.
Examples
--------
>>> import stumpy
>>> import numpy as np
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.atsc(mp[:, 2], mp[:, 3], 1)
array([1, 3])
>>> # Alternative example using named attributes
>>>
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.atsc(mp.left_I_, mp.right_I_, 1)
array([1, 3])
"""
C = deque([j])
for i in range(IL.size):
if IR[j] == -1 or IL[IR[j]] != j:
break
else:
j = IR[j]
C.append(j)
out = np.array(list(C), dtype=np.int64)
return out
[docs]
def allc(IL, IR):
"""
Compute the all-chain set (ALLC)
Note that since the matrix profile indices, ``IL`` and ``IR``, are pre-computed,
this function is agnostic to subsequence normalization.
Parameters
----------
IL : numpy.ndarray
Left matrix profile indices.
IR : numpy.ndarray
Right matrix profile indices.
Returns
-------
S : list(numpy.ndarray)
All-chain set.
C : numpy.ndarray
Anchored time series chain for the longest chain (also known as the unanchored
chain). Note that when there are multiple different chains with length equal to
``len(C)``, then only one chain from this set is returned. You may iterate over
the all-chain set, ``S``, to find all other possible chains with length
``len(C)``.
See Also
--------
stumpy.atsc : Compute the anchored time series chain (ATSC)
Notes
-----
`DOI: 10.1109/ICDM.2017.79 <https://www.cs.ucr.edu/~eamonn/chains_ICDM.pdf>`__
See Table II
Unlike the original paper, we've replaced the while-loop with a more stable
for-loop.
This is the implementation for the all-chain set (ALLC) and the unanchored
chain is simply the longest one among the all-chain set. Both the
all-chain set and unanchored chain are returned.
The all-chain set, ``S``, is returned as a list of unique numpy arrays.
Examples
--------
>>> import stumpy
>>> import numpy as np
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.allc(mp[:, 2], mp[:, 3])
([array([1, 3]), array([2]), array([0, 4])], array([0, 4]))
>>> # Alternative example using named attributes
>>>
>>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3)
>>> stumpy.allc(mp.left_I_, mp.right_I_)
([array([1, 3]), array([2]), array([0, 4])], array([0, 4]))
"""
L = np.ones(IL.size, dtype=np.int64)
S = set() # type: ignore
for i in range(IL.size):
if L[i] == 1:
j = i
C = deque([j])
for k in range(IL.size):
if IR[j] == -1 or IL[IR[j]] != j:
break
else:
j = IR[j]
L[j] = -1
L[i] = L[i] + 1
C.append(j)
S.update([tuple(C)])
C = atsc(IL, IR, L.argmax())
S = [np.array(s, dtype=np.int64) for s in S] # type: ignore
return S, C # type: ignore
