(self, a, size=None, replace=True, p=None, chunks="auto")
| 548 | |
| 549 | @derived_from(np.random.RandomState, skipblocks=1) |
| 550 | def choice(self, a, size=None, replace=True, p=None, chunks="auto"): |
| 551 | ( |
| 552 | a, |
| 553 | size, |
| 554 | replace, |
| 555 | p, |
| 556 | axis, # np.random.RandomState.choice does not use axis |
| 557 | chunks, |
| 558 | meta, |
| 559 | dependencies, |
| 560 | ) = _choice_validate_params(self, a, size, replace, p, 0, chunks) |
| 561 | |
| 562 | sizes = list(product(*chunks)) |
| 563 | state_data = random_state_data(len(sizes), self._numpy_state) |
| 564 | |
| 565 | name = ( |
| 566 | f"da.random.choice-{tokenize(state_data, size, chunks, a, replace, p)}" |
| 567 | ) |
| 568 | keys = product([name], *(range(len(bd)) for bd in chunks)) |
| 569 | dsk = { |
| 570 | k: Task(k, _choice_rs, state, a, size, replace, p) |
| 571 | for k, state, size in zip(keys, state_data, sizes) |
| 572 | } |
| 573 | |
| 574 | graph = HighLevelGraph.from_collections( |
| 575 | name, dsk, dependencies=dependencies |
| 576 | ) |
| 577 | return Array(graph, name, chunks, meta=meta) |
| 578 | |
| 579 | @derived_from(np.random.RandomState, skipblocks=1) |
| 580 | def exponential(self, scale=1.0, size=None, chunks="auto", **kwargs): |
no test coverage detected