Source code for stumpy.chains

# 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 -------- >>> mp = stumpy.stump(np.array([584., -11., 23., 79., 1001., 0., -19.]), m=3) >>> stumpy.atsc(mp[:, 2], mp[:, 3], 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 -------- >>> 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])) """ 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