| 265 | |
| 266 | |
| 267 | def test_stimp_1_percent_with_isconstant(): |
| 268 | T = np.random.uniform(-1, 1, [64]) |
| 269 | isconstant_func = functools.partial( |
| 270 | naive.isconstant_func_stddev_threshold, stddev_threshold=0.5 |
| 271 | ) |
| 272 | |
| 273 | threshold = 0.2 |
| 274 | percentage = 0.01 |
| 275 | min_m = 3 |
| 276 | n = T.shape[0] - min_m + 1 |
| 277 | |
| 278 | seed = np.random.randint(100000) |
| 279 | |
| 280 | np.random.seed(seed) |
| 281 | pan = stimp( |
| 282 | T, |
| 283 | min_m=min_m, |
| 284 | max_m=None, |
| 285 | step=1, |
| 286 | percentage=percentage, |
| 287 | pre_scrump=True, |
| 288 | # normalize=True, |
| 289 | T_subseq_isconstant_func=isconstant_func, |
| 290 | ) |
| 291 | |
| 292 | for i in range(n): |
| 293 | pan.update() |
| 294 | |
| 295 | ref_PAN = np.full((pan.M_.shape[0], T.shape[0]), fill_value=np.inf) |
| 296 | |
| 297 | np.random.seed(seed) |
| 298 | for idx, m in enumerate(pan.M_[:n]): |
| 299 | zone = int(np.ceil(m / 4)) |
| 300 | s = zone |
| 301 | tmp_P, tmp_I = naive.prescrump( |
| 302 | T, |
| 303 | m, |
| 304 | T, |
| 305 | s=s, |
| 306 | exclusion_zone=zone, |
| 307 | T_A_subseq_isconstant=isconstant_func, |
| 308 | T_B_subseq_isconstant=isconstant_func, |
| 309 | ) |
| 310 | ref_P, ref_I, _, _ = naive.scrump( |
| 311 | T, |
| 312 | m, |
| 313 | T, |
| 314 | percentage, |
| 315 | zone, |
| 316 | True, |
| 317 | s, |
| 318 | T_A_subseq_isconstant=isconstant_func, |
| 319 | T_B_subseq_isconstant=isconstant_func, |
| 320 | ) |
| 321 | naive.merge_topk_PI(ref_P, tmp_P, ref_I, tmp_I) |
| 322 | ref_PAN[pan._bfs_indices[idx], : ref_P.shape[0]] = ref_P |
| 323 | |
| 324 | # Compare raw pan |