Added unit test and parameters with type

graphs/uniform_search_cost.py

@@ -1,9 +1,11 @@
-from typing import List
 import heapq
+import doctest
 
 
 class UniformCostSearch:
-    def __init__(self, current: list, final: list, grid: list[list]) -> None:
+    def __init__(
+        self, current: list[int], final: list[int], grid: list[list[int]]
+    ) -> None:
         self.m = len(grid[0])
         self.n = len(grid)
 
@@ -76,8 +78,17 @@ class UniformCostSearch:
     ]
 
     def get_shortest_path(
-        self, start: list, end: list, dist: list[list], dxy: list[tuple]
-    ) -> list:
+        self,
+        start: list[int],
+        end: list[int],
+        dist: list[list[int]],
+        dxy: list[tuple],
+    ) -> 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)])
+        [[0,2],[1,2],[2,2]]
+        """
         shortest_path = []
         curr_node = end
         while curr_node != start:
@@ -100,13 +111,19 @@ class UniformCostSearch:
 
     def ucs(
         self,
-        current: list,
-        final: list[list],
-        grid: list[list],
-        prev: list[list],
+        current: list[int],
+        final: list[list[int]],
+        grid: list[list[int]],
+        prev: list[list[int]],
         dxy: list[tuple],
         goal_answer: list,
-    ) -> list:
+    ) -> 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])
+        [[0,2],[1,2],[2,2]]
+        """
+
         dist = [[float("inf") for _ in range(self.m)] for _ in range(self.n)]
         visited = [[0 for _ in range(self.m)] for _ in range(self.n)]
 
@@ -154,8 +171,13 @@ class UniformCostSearch:
                         heapq.heappush(heap, (new_dist, x + dx, y + dy))
 
     def your_algorithm(
-        self, start_point: list, end_point: list, grid: list[list]
-    ) -> list[int]:
+        self, start_point: list[int], end_point: list[int], grid: list[list[int]]
+    ) -> 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]])
+        [[0,2],[1,2],[2,2]]
+        """
         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:
@@ -178,6 +200,11 @@ class UniformCostSearch:
 
 
 def run() -> None:
+    """
+    Return None. Its just running the UCS algorithm class.
+    >>> run(2, 2)
+    None
+    """
     executed_object = UniformCostSearch(
         [0, 7],
         [19, 17],