[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

dynamic_programming/travelling_salesman_problem.py

@@ -1,29 +1,30 @@
 #!/usr/bin/env python3
 
+
 def tsp(distances: list[list[int]]) -> int:
     """
-    Solves the Travelling Salesman Problem (TSP)
-    using dynamic programming and bitmasking.
-
-    Args:
-        distances: 2D list where distances[i][j]
-        is the distance between city i and city j.
-
-    Returns:
-        Minimum cost to complete the tour visiting all cities.
-
-    Raises:
-        ValueError: If any distance is negative.
-
->>> tsp([[0, 10, 15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0]])
-    80
->>> tsp([[0, 29, 20, 21], [29, 0, 15, 17], [20, 15, 0, 28], [21, 17, 28, 0]])
-    69
->>> tsp([[0, 10, -15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0]])  
-# doctest: +ELLIPSIS
-    Traceback (most recent call last):
-        ...
-    ValueError: Distance cannot be negative
+        Solves the Travelling Salesman Problem (TSP)
+        using dynamic programming and bitmasking.
+
+        Args:
+            distances: 2D list where distances[i][j]
+            is the distance between city i and city j.
+
+        Returns:
+            Minimum cost to complete the tour visiting all cities.
+
+        Raises:
+            ValueError: If any distance is negative.
+
+    >>> tsp([[0, 10, 15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0]])
+        80
+    >>> tsp([[0, 29, 20, 21], [29, 0, 15, 17], [20, 15, 0, 28], [21, 17, 28, 0]])
+        69
+    >>> tsp([[0, 10, -15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0]])
+    # doctest: +ELLIPSIS
+        Traceback (most recent call last):
+            ...
+        ValueError: Distance cannot be negative
     """
     n = len(distances)
     if any(distances[i][j] < 0 for i in range(n) for j in range(n)):
@@ -40,7 +41,7 @@ def visit(city: int, mask: int) -> int:
         if memo[city][mask] != -1:  # Return cached result if exists
             return memo[city][mask]
 
-        min_cost = float('inf')  # Use infinity for initial comparison
+        min_cost = float("inf")  # Use infinity for initial comparison
         for next_city in range(n):
             if not (mask & (1 << next_city)):  # If unvisited
                 new_cost = distances[city][next_city] + visit(
@@ -49,8 +50,11 @@ def visit(city: int, mask: int) -> int:
                 min_cost = min(min_cost, new_cost)
         memo[city][mask] = min_cost  # Store result in the memoization table
         return min_cost
+
     return visit(0, 1)  # Start from city 0 with only city 0 visited
 
+
 if __name__ == "__main__":
     import doctest
+
     doctest.testmod()