46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
import random
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from collections.abc import Generator
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from typing import Never
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def sample_hyperspace(
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*args: tuple[int, int] | tuple[float, float], seed: int = 2010392991
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) -> Generator[tuple[float | int, ...], None, Never]:
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minimums: tuple[float | int, ...] = tuple(arg[0] for arg in args)
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maximums: tuple[float | int, ...] = tuple(arg[1] for arg in args)
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diffs: tuple[float | int, ...] = tuple(ma - mi for mi, ma in zip(minimums, maximums))
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idiffs: tuple[float, ...] = tuple(1.0 / diff for diff in diffs)
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rng: random.Random = random.Random(seed)
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previous_points: set[tuple[float | int, ...]] = set()
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def dist(point: tuple[float | int, ...], other: tuple[float | int, ...]) -> float:
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return sum(((p - o) * idiff) ** 2 for p, o, idiff in zip(point, other, idiffs))
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while True:
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sampled_points: list[tuple[float | int, ...]] = [
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tuple(
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min
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+ (
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rng.randint(min, min + diff)
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if isinstance(min, int) and isinstance(diff, int)
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else rng.uniform(min, min + diff)
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)
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for min, diff in zip(minimums, diffs)
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)
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for _ in range(10)
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]
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if len(previous_points) == 0:
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previous_points.add(sampled_points[0])
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yield sampled_points[0]
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min_distances: list[float] = [
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min((dist(point, other) for other in previous_points)) for point in sampled_points
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]
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mdist: float = max(min_distances)
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point: tuple[float | int, ...] = sampled_points[[i for i, j in enumerate(min_distances) if j == mdist][0]]
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previous_points.add(point)
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yield point
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