def _random_benchmark(self,
random_state: np.random.Generator) -> Benchmark:
seed = random_state.integers(UINT_MAX)
return self.benchmark_from_seed(seed)
def fixture_x0(rng: np.random.Generator, x0_shape: ShapeType) -> np.ndarray:
"""Random data from a standard normal distribution."""
return rng.normal(0, 1, size=x0_shape)
def _sample_truncated_integer_gaussian(rng: np.random.Generator, loc: int, scale: int, min_val: int, max_val: int) -> int:
sample = None
while sample is None or not (min_val <= sample <= max_val):
sample = int(rng.normal(loc=loc, scale=scale))
return sample
