diff options
Diffstat (limited to 'src/eval.py')
| -rw-r--r-- | src/eval.py | 23 |
1 files changed, 8 insertions, 15 deletions
diff --git a/src/eval.py b/src/eval.py index ad1d7f3..191ac79 100644 --- a/src/eval.py +++ b/src/eval.py @@ -57,8 +57,10 @@ def _sieve_rank_signature(k: int, lo: int, hi: int) -> set[int]: def _compute_sieve_rank(preds: list[int], lo: int, hi: int) -> int | None: - """Find the smallest composite predicted as 'prime' with all factors > p_k. + """Find the most specific sieve rank that explains the model's error predictions. Returns k (the number of primes in the sieve, 1-indexed) or None. + A rank-k sieve checks {2,3,...,p_k} and misses composites with all factors > p_k. + We want the LARGEST k whose signature set contains the model's error predictions. """ primes = sieve_primes(hi + 100) composites_in_range = set() @@ -77,21 +79,12 @@ def _compute_sieve_rank(preds: list[int], lo: int, hi: int) -> int | None: pred_composites = sorted(composites_in_range & set(preds)) if not pred_composites: return None # no composites predicted — either exact or garbage - smallest = pred_composites[0] - # find k such that all factors of smallest are > primes[k-1] - for k in range(1, 50): + # find the LARGEST k whose rank-k signature contains all error predictions + for k in range(40, 0, -1): if k >= len(primes): - return None - pk = primes[k - 1] - # check if ANY prime ≤ pk divides smallest - has_small_factor = False - for p in primes: - if p > pk: - break - if smallest % p == 0: - has_small_factor = True - break - if not has_small_factor: + continue + sig = _sieve_rank_signature(k, lo, hi) + if all(p in sig for p in pred_composites): return k return None |