Lecture 7

SoSe24/lec06_graph/astar.py

@@ -2,7 +2,7 @@ import math
 from typing import Callable
 import re
 from graph import Graph, AdjacencyListGraph, AdjacencyMatrixGraph, NodeColor, Vertex
-
+import heapq
 
 def a_star(self, start_name: str, end_name: str, heuristic: Callable[[Vertex], float]):
     color_map = {}                  # maps vertices to their color
@@ -15,6 +15,8 @@ def a_star(self, start_name: str, end_name: str, heuristic: Callable[[Vertex], f
             return math.inf
         return distance_map[vertex] + heuristic(vertex)
 
+    Vertex.__lt__ = lambda self, other: cost(self) < cost(other)
+
     # Initialize the maps
     for vertex in self.all_vertices():
         color_map[vertex] = NodeColor.WHITE
@@ -27,12 +29,15 @@ def a_star(self, start_name: str, end_name: str, heuristic: Callable[[Vertex], f
     distance_map[start_node] = 0
 
     # Initialize the queue with the start vertex
-    queue = [start_node]
+    queue = [] # Use a list instead of a deque, because we need to sort it with
+    heapq.heappush(queue,  start_node)
 
     # Process the queue
     while len(queue) > 0:
-        queue.sort(key=cost)
-        vertex = queue.pop(0)
+        vertex = heapq.heappop(queue)
+        if color_map[vertex] == NodeColor.BLACK:
+            # Skip already processed vertices (possibly with a higher cost)
+            continue
         if vertex.value == end_name:
             # Return the distance and predecessor maps
             return distance_map, predecessor_map
@@ -44,8 +49,8 @@ def a_star(self, start_name: str, end_name: str, heuristic: Callable[[Vertex], f
                 continue
             predecessor_map[dest] = vertex
             distance_map[dest] = distance_map[vertex] + weight
+            heapq.heappush(queue, dest)
             if color_map[dest] == NodeColor.WHITE:
-                queue.append(dest)
                 color_map[dest] = NodeColor.GRAY
         color_map[vertex] = NodeColor.BLACK