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src/sampling_hyperparams.py

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