diff --git a/src/kemeny_young.py b/src/kemeny_young.py
index 34b4100..17b8935 100644
--- a/src/kemeny_young.py
+++ b/src/kemeny_young.py
@@ -23,7 +23,8 @@ from tprint import tprint
 
 def kendall_tau_dist(ranking_a: list[int], ranking_b: list[int]) -> Number:
     """The kendall τ distance between two rankings / permutations.
-    It is the number of inversions that don't have the same sign within all pairs of an inversion of ranking_a and an inversion of ranking_b.
+    It is the number of inversions that don't have the same sign within all
+    pairs of an inversion of ranking_a and an inversion of ranking_b.
     """
     ranking_a = np.array(ranking_a)
     ranking_b = np.array(ranking_b)
diff --git a/src/orderankings.py b/src/orderankings.py
index 64a5d7b..1a5d00c 100644
--- a/src/orderankings.py
+++ b/src/orderankings.py
@@ -58,7 +58,7 @@ def get_orderings_from_table(table: np.ndarray, column_index: int = 0) -> list:
     return values[np.sort(indexes)]  # distinct ordered values
 
 
-def get_all_orderings_from_table(table: list[list[str]]) -> dict:
+def get_all_orderings_from_table(table: list[list[str]]) -> dict[str, list[str]]:
     """Return a dictionnary mapping a value of the criteria to the order you
     get when selecting on this value.
     This means you get all orders of a table, where the criteria is in the
diff --git a/src/tools.py b/src/tools.py
index 541cfb4..3af2bce 100644
--- a/src/tools.py
+++ b/src/tools.py
@@ -4,7 +4,7 @@ from fastcache import lru_cache
 
 Number = int | float
 
-# @lru_cache(maxsize=16)
+@lru_cache(maxsize=4)
 def combinations_of_2(size: int):
     """Returns an array of size n*2, containing every pair of two integers
     smaller than size, but not listing twice the pairs with the same numbers
@@ -21,7 +21,7 @@ def __combinations_of_2(size: int):
     """Compiled helper."""
     # return np.array(list(combinations(range(size), 2)))
     # return np.array(np.meshgrid(np.arange(size), np.arange(size))).T.reshape(-1, 2)
-    return np.array([[i, j] for i in range(0, size) for j in range(0, size) if i<j])
+    return np.array([[i, j] for i in range(size) for j in range(size) if i<j])