test: capture mouse generate() golden output (pre-migration)

scripts/build_golden_mouse.py

@@ -0,0 +1,49 @@
+"""One-shot script to capture golden mouse trajectories from the current torch
+implementation. Run BEFORE the migration so we can verify the numpy/ORT rewrite
+in Phase 4 produces equivalent output.
+
+Output: tests/unit/data/golden_mouse.npz
+"""
+from __future__ import annotations
+
+import random
+import sys
+from pathlib import Path
+
+# Allow running as `uv run python scripts/build_golden_mouse.py` from project root.
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+import numpy as np
+import torch
+
+from ai_mouse import generate
+
+CASES: list[tuple[tuple[int, int], tuple[int, int]]] = [
+    ((100, 200), (900, 400)),    # horizontal 800px
+    ((500, 500), (500, 100)),    # vertical 400px upward
+    ((200, 600), (800, 200)),    # 720px diagonal
+    ((100, 100), (130, 110)),    # very short 31px
+    ((50,  50),  (1500, 900)),   # very long 1700px
+    ((400, 300), (500, 300)),    # short horizontal 100px
+    ((300, 300), (700, 700)),    # 45° diagonal
+    ((600, 400), (200, 100)),    # reverse diagonal
+]
+SEEDS = (0, 1, 2, 3)
+
+
+def main() -> None:
+    out: dict[str, np.ndarray] = {}
+    for case_idx, (start, end) in enumerate(CASES):
+        for seed in SEEDS:
+            random.seed(seed)
+            np.random.seed(seed)
+            torch.manual_seed(seed)
+            pts = generate(start=start, end=end)
+            out[f"case{case_idx}_seed{seed}"] = np.array(pts, dtype=np.int64)
+    out_path = Path("tests/unit/data/golden_mouse.npz")
+    np.savez_compressed(out_path, **out)
+    print(f"Wrote {len(out)} golden traces to {out_path}")
+
+
+if __name__ == "__main__":
+    main()