diff --git a/learning.py b/learning.py
index df5d6fce3..3e7f4690c 100644
--- a/learning.py
+++ b/learning.py
@@ -746,7 +746,7 @@ def weighted_replicate(seq, weights, n):
     wholes = [int(w * n) for w in weights]
     fractions = [(w * n) % 1 for w in weights]
     return (flatten([x] * nx for x, nx in zip(seq, wholes)) +
-            weighted_sample_with_replacement(seq, fractions, n - sum(wholes)))
+            weighted_sample_with_replacement(n - sum(wholes),seq, fractions, ))
 
 
 def flatten(seqs): return sum(seqs, [])
diff --git a/probability.py b/probability.py
index abbc07791..d28a8a38b 100644
--- a/probability.py
+++ b/probability.py
@@ -651,5 +651,5 @@ def particle_filtering(e, N, HMM):
         w[i] = float("{0:.4f}".format(w[i]))
 
     # STEP 2
-    s = weighted_sample_with_replacement(s, w, N)
+    s = weighted_sample_with_replacement(N,s,w)
     return s
diff --git a/search.py b/search.py
index 04d5b6c51..e4e20da3f 100644
--- a/search.py
+++ b/search.py
@@ -587,7 +587,7 @@ def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1):
         new_population = []
         for i in range(len(population)):
             fitnesses = map(fitness_fn, population)
-            p1, p2 = weighted_sample_with_replacement(population, fitnesses, 2)
+            p1, p2 = weighted_sample_with_replacement(2,population, fitnesses)
             child = p1.mate(p2)
             if random.uniform(0, 1) < pmut:
                 child.mutate()
diff --git a/utils.py b/utils.py
index 3c070293e..714512ae0 100644
--- a/utils.py
+++ b/utils.py
@@ -193,7 +193,7 @@ def probability(p):
     return p > random.uniform(0.0, 1.0)
 
 
-def weighted_sample_with_replacement(seq, weights, n):
+def weighted_sample_with_replacement(n,seq, weights):
     """Pick n samples from seq at random, with replacement, with the
     probability of each element in proportion to its corresponding
     weight."""