perf(prob): speed-up simulator

estimator/prob.py

@@ -77,7 +77,7 @@ def gaussian_cdf(mu, sigma, t):
     return RR((1/2)*(1 + erf((t - mu)/(sqrt(2)*sigma))))
 
 
-def mitm_babai_probability(r, stddev, fast=False):
+def mitm_babai_probability(r, stddev, fast=True):
     """
     Compute the "e-admissibility" probability associated to the mitm step, according to
     [WAHC:SonChe19]_
@@ -94,7 +94,8 @@ def mitm_babai_probability(r, stddev, fast=False):
     # Note: `r` contains *square norms*, so convert to non-square norms.
     # Follow the proof of Lemma 4.2 [WAHC:SonChe19]_, because that one uses standard deviation.
     xs = [sqrt(.5 * ri) / stddev for ri in r]
-    p = prod(RR(erf(x) - (1 - exp(-x**2)) / (x * sqrt(pi))) for x in xs)
+    p_list = [RR(erf(x) - (1 - exp(-x**2)) / (x * sqrt(pi))) for x in xs]
+    p = prod(p_list)
     assert 0.0 <= p <= 1.0
     return p