Changed doctest for ruff failed

graphs/uniform_search_cost.py

@@ -1,13 +1,4 @@
 import heapq
-import doctest
-
-
-class UniformCostSearch:
-    def __init__(
-        self, current: list[int], final: list[int], grid: list[list[int]]
-    ) -> None:
-        self.m = len(grid[0])
-        self.n = len(grid)
 
 # diagonal clockwise
 dxy1 = [
@@ -77,6 +68,12 @@ class UniformCostSearch:
     (-1, 1),
 ]
 
+
+class UniformCostSearch:
+    def __init__(self, grid: list[list[int]]) -> None:
+        self.m = len(grid[0])
+        self.n = len(grid)
+
     def get_shortest_path(
         self,
         start: list[int],
@@ -86,8 +83,12 @@ class UniformCostSearch:
     ) -> list[list[int]]:
         """
         Return 2D list where optimal path is stored.
-        >>> get_shortest_path([0, 2],[2, 2], [['inf','inf',1],['inf,2,2],['inf',0,3]], [(1, 1),(1, 0),(1, -1),(0, -1),(-1, -1),(-1, 0),(-1, 1),(0, 1)])
+        >>> start = [0,0]
-        [[0,2],[1,2],[2,2]]
+        >>> end = [1, 2]
+        >>> dist = [[1,0],[0,2]]
+        >>> dxy = [(1, 1),(1, 0),(1, -1),(0, -1),(-1, -1),(-1, 0),(-1, 1),(0, 1)]
+        >>> get_shortest_path(start,end,dist,dxy)
+        [[0,0],[1,1]]
         """
         shortest_path = []
         curr_node = end
@@ -120,8 +121,14 @@ class UniformCostSearch:
     ) -> list[list[int]]:
         """
         Return 2D list where optimal path is stored.
-        >>> ucs([0, 2],[[1,2],[2, 2]], [[0,0,0],[0,0,0],[0,0,0]], [[None, None, None], [None, None, None],[None, None, None]], [(1, 1),(1, 0),(1, -1),(0, -1),(-1, -1),(-1, 0),(-1, 1),(0, 1)], [1000000, 100000])
+        >>> current = [0, 0]
-        [[0,2],[1,2],[2,2]]
+        >>> final = [[1, 1]]
+        >>> grid = [[0,0],[0,0]]
+        >>> prev = [[None, None],[None, None]]
+        >>> dxy = [(1, 1),(1, 0),(1, -1),(0, -1),(-1, -1),(-1, 0),(-1, 1),(0, 1)]
+        >>> goal_answer = [1000000, 100000]
+        >>> ucs(current,final,grid,dxy,prev,goal_answer)
+        [[0,2],[1,1]]
         """
 
         dist = [[float("inf") for _ in range(self.m)] for _ in range(self.n)]
@@ -175,25 +182,28 @@ class UniformCostSearch:
     ) -> list[list[int]]:
         """
         Return 2D list where optimal path is stored.
-        >>> your_algorithm([0, 2],[2, 2], [[0,0,0],[0,0,0],[0,0,0]])
+        >>> start_point = [0, 0]
-        [[0,2],[1,2],[2,2]]
+        >>> end_point = [1, 1]
+        >>> grid = [[0,0],[0,0]]
+        >>> your_algorithm(start_point, end_point, grid)
+        [[0,0],[1,1]]
         """
         prev = [[None for _ in range(self.m)] for _ in range(self.n)]
         dxy = []
         if start_point[1] - end_point[1] == 0 and start_point[0] - end_point[0] < 0:
-            dxy = self.dxy5
+            dxy = dxy5
         elif start_point[1] - end_point[1] == 0 and start_point[0] - end_point[0] > 0:
-            dxy = self.dxy6
+            dxy = dxy6
         elif start_point[0] - end_point[0] == 0 and start_point[1] - end_point[1] < 0:
-            dxy = self.dxy4
+            dxy = dxy4
         elif start_point[0] - end_point[0] == 0 and start_point[1] - end_point[1] > 0:
-            dxy = self.dxy3
+            dxy = dxy3
         elif start_point[0] - end_point[0] > 0:
-            dxy = self.dxy2
+            dxy = dxy2
         elif start_point[0] - end_point[0] < 0:
-            dxy = self.dxy1
+            dxy = dxy1
         goal_answer = []
-        for cell in end_point:
+        for _ in range(0, len(end_point)):
             goal_answer.append(10**8)
         path = self.ucs(start_point, [end_point], grid, prev, dxy, goal_answer)
         return path
@@ -202,12 +212,10 @@ class UniformCostSearch:
 def run() -> None:
     """
     Return None. Its just running the UCS algorithm class.
-    >>> run(2, 2)
+    >>> run()
     None
     """
     executed_object = UniformCostSearch(
-        [0, 7],
-        [19, 17],
         [
             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],