Use hamming weight instead of own computation

estimator/lwe_guess.py

@@ -102,7 +102,7 @@ def sparse_solve(cls, f, params, log_level=5, **kwds):
         :param params: LWE parameters.
         """
         base = params.Xs.bounds[1] - params.Xs.bounds[0]  # we exclude zero
-        h = ceil(len(params.Xs) * params.Xs.density)  # nr of non-zero entries
+        h = params.Xs.hamming_weight
 
         with local_minimum(0, params.n - 40, log_level=log_level) as it:
             for zeta in it:

estimator/lwe_parameters.py

@@ -140,10 +140,10 @@ def switch_modulus(self):
             LWEParameters(n=128, q=5289, Xs=D(σ=0.82), Xe=D(σ=3.08), m=+Infinity, tag=None)
 
         """
-        n = self.Xs.density * len(self.Xs)
+        h = self.Xs.hamming_weight
 
-        # n uniform in -(0.5,0.5) ± stddev(χ_s)
-        Xr_stddev = sqrt(n / 12) * self.Xs.stddev  # rounding noise
+        # h uniform in -(0.5,0.5) ± stddev(χ_s)
+        Xr_stddev = sqrt(h / 12) * self.Xs.stddev  # rounding noise
         # χ_r == p/q ⋅ χ_e # we want the rounding noise match the scaled noise
         p = ceil(Xr_stddev * self.q / self.Xe.stddev)
 

estimator/lwe_primal.py

@@ -390,7 +390,7 @@ def ssf(x):
 
         if zeta:
             # the number of non-zero entries
-            h = ceil(len(params.Xs) * params.Xs.density)
+            h = params.Xs.hamming_weight
             probability = RR(prob_drop(params.n, h, zeta))
             hw = 1
             while hw < min(h, zeta):