Compute the Pan Matrix Profile This is based on the SKIMP algorithm. Parameters ---------- T : numpy.ndarray The time series or sequence for which to compute the pan matrix profile m_start : int, default 3 The starting (or minimum) subsequence window size
| 408 | |
| 409 | |
| 410 | class aamp_stimp(_aamp_stimp): |
| 411 | """ |
| 412 | Compute the Pan Matrix Profile |
| 413 | |
| 414 | This is based on the SKIMP algorithm. |
| 415 | |
| 416 | Parameters |
| 417 | ---------- |
| 418 | T : numpy.ndarray |
| 419 | The time series or sequence for which to compute the pan matrix profile |
| 420 | |
| 421 | m_start : int, default 3 |
| 422 | The starting (or minimum) subsequence window size for which a matrix profile |
| 423 | may be computed |
| 424 | |
| 425 | m_stop : int, default None |
| 426 | The stopping (or maximum) subsequence window size for which a matrix profile |
| 427 | may be computed. When `m_stop = None`, this is set to the maximum allowable |
| 428 | subsequence window size |
| 429 | |
| 430 | m_step : int, default 1 |
| 431 | The step between subsequence window sizes |
| 432 | |
| 433 | percentage : float, default 0.01 |
| 434 | The percentage of the full matrix profile to compute for each subsequence |
| 435 | window size. When `percentage < 1.0`, then the `scraamp` algorithm is used. |
| 436 | Otherwise, the `stump` algorithm is used when the exact matrix profile is |
| 437 | requested. |
| 438 | |
| 439 | pre_scraamp : bool, default True |
| 440 | A flag for whether or not to perform the PreSCRIMP calculation prior to |
| 441 | computing SCRIMP. If set to `True`, this is equivalent to computing |
| 442 | SCRIMP++. This parameter is ignored when `percentage = 1.0`. |
| 443 | |
| 444 | p : float, default 2.0 |
| 445 | The p-norm to apply for computing the Minkowski distance. Minkowski distance is |
| 446 | typically used with `p` being 1 or 2, which correspond to the Manhattan distance |
| 447 | and the Euclidean distance, respectively. |
| 448 | |
| 449 | Attributes |
| 450 | ---------- |
| 451 | PAN_ : numpy.ndarray |
| 452 | The transformed (i.e., normalized, contrasted, binarized, and repeated) |
| 453 | pan matrix profile |
| 454 | |
| 455 | M_ : numpy.ndarray |
| 456 | The full list of (breadth first search (level) ordered) subsequence window |
| 457 | sizes |
| 458 | |
| 459 | Methods |
| 460 | ------- |
| 461 | update(): |
| 462 | Compute the next matrix profile using the next available (breadth-first-search |
| 463 | (level) ordered) subsequence window size and update the pan matrix profile |
| 464 | |
| 465 | Notes |
| 466 | ----- |
| 467 | `DOI: 10.1109/ICBK.2019.00031 \ |
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