Fixes in Bidirectional A* (#2020)

* implement bidirectional astar

* add type hints

* add wikipedia url

* format with black

* changes from review

* fix collision check

* Add testmod()

* # doctest: +NORMALIZE_WHITESPACE

* Codespell: euclidean

* Codespell: coordinates

* Codespell: traversal

* Codespell: remaining

Co-authored-by: John Law <johnlaw.po@gmail.com>
Co-authored-by: Christian Clauss <cclauss@me.com>

graphs/bidirectional_a_star.py

@@ -3,8 +3,12 @@ https://en.wikipedia.org/wiki/Bidirectional_search
 """
 
 import time
+from math import sqrt
 from typing import List, Tuple
 
+# 1 for manhattan, 0 for euclidean
+HEURISTIC = 0
+
 grid = [
     [0, 0, 0, 0, 0, 0, 0],
     [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
@@ -20,12 +24,12 @@ delta = [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right
 
 class Node:
     """
-    >>> k = Node(0, 0, 4, 5, 0, None)
+    >>> k = Node(0, 0, 4, 3, 0, None)
     >>> k.calculate_heuristic()
-    9
+    5.0
     >>> n = Node(1, 4, 3, 4, 2, None)
     >>> n.calculate_heuristic()
-    2
+    2.0
     >>> l = [k, n]
     >>> n == l[0]
     False
@@ -47,18 +51,35 @@ class Node:
 
     def calculate_heuristic(self) -> float:
         """
-        The heuristic here is the Manhattan Distance
-        Could elaborate to offer more than one choice
+        Heuristic for the A*
         """
-        dy = abs(self.pos_x - self.goal_x)
-        dx = abs(self.pos_y - self.goal_y)
-        return dx + dy
+        dy = self.pos_x - self.goal_x
+        dx = self.pos_y - self.goal_y
+        if HEURISTIC == 1:
+            return abs(dx) + abs(dy)
+        else:
+            return sqrt(dy ** 2 + dx ** 2)
 
-    def __lt__(self, other):
+    def __lt__(self, other) -> bool:
         return self.f_cost < other.f_cost
 
 
 class AStar:
+    """
+    >>> astar = AStar((0, 0), (len(grid) - 1, len(grid[0]) - 1))
+    >>> (astar.start.pos_y + delta[3][0], astar.start.pos_x + delta[3][1])
+    (0, 1)
+    >>> [x.pos for x in astar.get_successors(astar.start)]
+    [(1, 0), (0, 1)]
+    >>> (astar.start.pos_y + delta[2][0], astar.start.pos_x + delta[2][1])
+    (1, 0)
+    >>> astar.retrace_path(astar.start)
+    [(0, 0)]
+    >>> astar.search()  # doctest: +NORMALIZE_WHITESPACE
+    [(0, 0), (1, 0), (2, 0), (2, 1), (2, 2), (2, 3), (3, 3),
+     (4, 3), (4, 4), (5, 4), (5, 5), (6, 5), (6, 6)]
+    """
+
     def __init__(self, start, goal):
         self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
         self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
@@ -68,10 +89,7 @@ class AStar:
 
         self.reached = False
 
-        self.path = [(self.start.pos_y, self.start.pos_x)]
-        self.costs = [0]
-
-    def search(self):
+    def search(self) -> List[Tuple[int]]:
         while self.open_nodes:
             # Open Nodes are sorted using __lt__
             self.open_nodes.sort()
@@ -79,8 +97,7 @@ class AStar:
 
             if current_node.pos == self.target.pos:
                 self.reached = True
-                self.path = self.retrace_path(current_node)
-                break
+                return self.retrace_path(current_node)
 
             self.closed_nodes.append(current_node)
             successors = self.get_successors(current_node)
@@ -101,7 +118,7 @@ class AStar:
                         self.open_nodes.append(better_node)
 
         if not (self.reached):
-            print("No path found")
+            return [(self.start.pos)]
 
     def get_successors(self, parent: Node) -> List[Node]:
         """
@@ -111,13 +128,14 @@ class AStar:
         for action in delta:
             pos_x = parent.pos_x + action[1]
             pos_y = parent.pos_y + action[0]
-            if not (0 < pos_x < len(grid[0]) - 1 and 0 < pos_y < len(grid) - 1):
+            if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
                 continue
 
             if grid[pos_y][pos_x] != 0:
                 continue
 
-            node_ = Node(
+            successors.append(
+                Node(
                     pos_x,
                     pos_y,
                     self.target.pos_y,
@@ -125,7 +143,7 @@ class AStar:
                     parent.g_cost + 1,
                     parent,
                 )
-            successors.append(node_)
+            )
         return successors
 
     def retrace_path(self, node: Node) -> List[Tuple[int]]:
@@ -142,13 +160,24 @@ class AStar:
 
 
 class BidirectionalAStar:
+    """
+    >>> bd_astar = BidirectionalAStar((0, 0), (len(grid) - 1, len(grid[0]) - 1))
+    >>> bd_astar.fwd_astar.start.pos == bd_astar.bwd_astar.target.pos
+    True
+    >>> bd_astar.retrace_bidirectional_path(bd_astar.fwd_astar.start,
+    ...                                     bd_astar.bwd_astar.start)
+    [(0, 0)]
+    >>> bd_astar.search()  # doctest: +NORMALIZE_WHITESPACE
+    [(0, 0), (0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (2, 4),
+     (2, 5), (3, 5), (4, 5), (5, 5), (5, 6), (6, 6)]
+    """
+
     def __init__(self, start, goal):
         self.fwd_astar = AStar(start, goal)
         self.bwd_astar = AStar(goal, start)
         self.reached = False
-        self.path = self.fwd_astar.path
 
-    def search(self):
+    def search(self) -> List[Tuple[int]]:
         while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
             self.fwd_astar.open_nodes.sort()
             self.bwd_astar.open_nodes.sort()
@@ -157,8 +186,9 @@ class BidirectionalAStar:
 
             if current_bwd_node.pos == current_fwd_node.pos:
                 self.reached = True
-                self.retrace_bidirectional_path(current_fwd_node, current_bwd_node)
-                break
+                return self.retrace_bidirectional_path(
+                    current_fwd_node, current_bwd_node
+                )
 
             self.fwd_astar.closed_nodes.append(current_fwd_node)
             self.bwd_astar.closed_nodes.append(current_bwd_node)
@@ -189,17 +219,25 @@ class BidirectionalAStar:
                         else:
                             astar.open_nodes.append(better_node)
 
+        if not self.reached:
+            return [self.fwd_astar.start.pos]
+
     def retrace_bidirectional_path(
         self, fwd_node: Node, bwd_node: Node
     ) -> List[Tuple[int]]:
         fwd_path = self.fwd_astar.retrace_path(fwd_node)
         bwd_path = self.bwd_astar.retrace_path(bwd_node)
-        fwd_path.reverse()
+        bwd_path.pop()
+        bwd_path.reverse()
         path = fwd_path + bwd_path
         return path
 
 
+if __name__ == "__main__":
     # all coordinates are given in format [y,x]
+    import doctest
+
+    doctest.testmod()
     init = (0, 0)
     goal = (len(grid) - 1, len(grid[0]) - 1)
     for elem in grid:
@@ -207,12 +245,12 @@ for elem in grid:
 
     start_time = time.time()
     a_star = AStar(init, goal)
-a_star.search()
+    path = a_star.search()
     end_time = time.time() - start_time
     print(f"AStar execution time = {end_time:f} seconds")
 
     bd_start_time = time.time()
     bidir_astar = BidirectionalAStar(init, goal)
-bidir_astar.search()
+    path = bidir_astar.search()
     bd_end_time = time.time() - bd_start_time
     print(f"BidirectionalAStar execution time = {bd_end_time:f} seconds")

machine_learning/astar.py

@@ -17,7 +17,7 @@ class Cell(object):
     """
     Class cell represents a cell in the world which have the property
     position : The position of the represented by  tupleof x and y
-    co-ordinates initially set to (0,0)
+    coordinates initially set to (0,0)
     parent : This contains the parent cell object which we visited
     before arrinving this cell
     g,h,f : The parameters for constructing the heuristic function

scheduling/shortest_job_first_algorithm.py

@@ -1,5 +1,5 @@
 """
-Shortest job remainig first
+Shortest job remaining first
 Please note arrival time and burst
 Please use spaces to separate times entered.
 """

sorts/tree_sort.py

@@ -29,7 +29,7 @@ class node:
 
 
 def inorder(root, res):
-    # Recursive travesal
+    # Recursive traversal
     if root:
         inorder(root.left, res)
         res.append(root.val)