A Numba JIT-compiled version of STOMPopt with Pearson correlations for parallel computation of the (top-k) matrix profile, the (top-k) matrix profile indices, the top-1 left matrix profile and its matrix profile index, and the top-1 right matrix profile and its matrix profile index.
(
T_A,
T_B,
m,
μ_Q,
M_T,
σ_Q_inverse,
Σ_T_inverse,
μ_Q_m_1,
M_T_m_1,
T_A_subseq_isfinite,
T_B_subseq_isfinite,
T_A_subseq_isconstant,
T_B_subseq_isconstant,
diags,
ignore_trivial,
k,
)
| 250 | fastmath=config.STUMPY_FASTMATH_FLAGS, |
| 251 | ) |
| 252 | def _stump( |
| 253 | T_A, |
| 254 | T_B, |
| 255 | m, |
| 256 | μ_Q, |
| 257 | M_T, |
| 258 | σ_Q_inverse, |
| 259 | Σ_T_inverse, |
| 260 | μ_Q_m_1, |
| 261 | M_T_m_1, |
| 262 | T_A_subseq_isfinite, |
| 263 | T_B_subseq_isfinite, |
| 264 | T_A_subseq_isconstant, |
| 265 | T_B_subseq_isconstant, |
| 266 | diags, |
| 267 | ignore_trivial, |
| 268 | k, |
| 269 | ): |
| 270 | """ |
| 271 | A Numba JIT-compiled version of STOMPopt with Pearson correlations for parallel |
| 272 | computation of the (top-k) matrix profile, the (top-k) matrix profile indices, |
| 273 | the top-1 left matrix profile and its matrix profile index, and the top-1 right |
| 274 | matrix profile and its matrix profile index. |
| 275 | |
| 276 | Parameters |
| 277 | ---------- |
| 278 | T_A : numpy.ndarray |
| 279 | The time series or sequence for which to compute the matrix profile |
| 280 | |
| 281 | T_B : numpy.ndarray |
| 282 | The time series or sequence that will be used to annotate `T_A`. For every |
| 283 | subsequence in `T_A`, its nearest neighbor in `T_B` will be recorded. |
| 284 | |
| 285 | m : int |
| 286 | Window size |
| 287 | |
| 288 | μ_Q : numpy.ndarray |
| 289 | Mean of the query sequence, `Q`, relative to the current sliding window |
| 290 | |
| 291 | M_T : numpy.ndarray |
| 292 | Sliding mean of time series, `T` |
| 293 | |
| 294 | σ_Q_inverse : numpy.ndarray |
| 295 | Inverse standard deviation of the query sequence, `Q`, relative to the current |
| 296 | sliding window |
| 297 | |
| 298 | Σ_T_inverse : numpy.ndarray |
| 299 | Inverse sliding standard deviation of time series, `T` |
| 300 | |
| 301 | μ_Q_m_1 : numpy.ndarray |
| 302 | Mean of the query sequence, `Q`, relative to the current sliding window and |
| 303 | using a window size of `m-1` |
| 304 | |
| 305 | M_T_m_1 : numpy.ndarray |
| 306 | Sliding mean of time series, `T`, using a window size of `m-1` |
| 307 | |
| 308 | T_A_subseq_isfinite : numpy.ndarray |
| 309 | A boolean array that indicates whether a subsequence in `T_A` contains a |
no test coverage detected