add perf_ceiling in rating computation

judge/ratings.py

@@ -1,5 +1,5 @@
 from bisect import bisect
-from math import pi, sqrt, tanh
+from math import inf, pi, sqrt, tanh
 from operator import attrgetter, itemgetter
 
 from django.db import transaction
@@ -19,6 +19,7 @@
 VAR_LIM = (sqrt(VAR_PER_CONTEST**2 + 4 * BETA2 * VAR_PER_CONTEST) - VAR_PER_CONTEST) / 2
 SD_LIM = sqrt(VAR_LIM)
 TANH_C = sqrt(3) / pi
+PERF_CEILING_INCREMENT = 400
 
 
 def tie_ranker(iterable, key=attrgetter('points')):
@@ -77,17 +78,19 @@ def get_var(times_ranked, cache=[VAR_INIT]):
     return cache[times_ranked]
 
 
-def recalculate_ratings(ranking, old_mean, times_ranked, historical_p):
+def recalculate_ratings(ranking, old_mean, times_ranked, historical_p, perf_ceiling):
     n = len(ranking)
     new_p = [0.] * n
     new_mean = [0.] * n
 
+    updated_bounds = [VALID_RANGE[0], min(VALID_RANGE[1], perf_ceiling)]
+
     # Note: pre-multiply delta by TANH_C to improve efficiency.
     delta = [TANH_C * sqrt(get_var(t) + VAR_PER_CONTEST + BETA2) for t in times_ranked]
     p_tanh_terms = [(m, d, 1) for m, d in zip(old_mean, delta)]
 
     # Calculate performance at index i.
-    def solve_idx(i, bounds=VALID_RANGE):
+    def solve_idx(i, bounds):
         r = ranking[i]
         y_tg = 0
         for d, s in zip(delta, ranking):
@@ -111,8 +114,8 @@ def divconq(i, j):
         new_mean = list(old_mean)
     else:
         # Calculate performance.
-        solve_idx(0)
-        solve_idx(n - 1)
+        solve_idx(0, updated_bounds)
+        solve_idx(n - 1, updated_bounds)
         divconq(0, n - 1)
 
         # Calculate mean.
@@ -160,6 +163,9 @@ def rate_contest(contest):
         users = users.exclude(last_rating__lt=contest.rating_floor)
     if contest.rating_ceiling is not None:
         users = users.exclude(last_rating__gt=contest.rating_ceiling)
+        perf_ceiling = contest.rating_ceiling + PERF_CEILING_INCREMENT
+    else:
+        perf_ceiling = inf
 
     users = list(users)
     participation_ids = list(map(itemgetter('id'), users))
@@ -176,7 +182,7 @@ def rate_contest(contest):
         idx = user_id_to_idx[h['user_id']]
         historical_p[idx].append(h['performance'])
 
-    rating, mean, performance = recalculate_ratings(ranking, old_mean, times_ranked, historical_p)
+    rating, mean, performance = recalculate_ratings(ranking, old_mean, times_ranked, historical_p, perf_ceiling)
 
     now = timezone.now()
     ratings = [Rating(user_id=i, contest=contest, rating=r, mean=m, performance=perf,