Question

USING PYTHON ONLY : I need to implement this pseudocode of a star (A*) searching algorithm :

# Let pq be an empty min priority queue'

# g(start) = 0

# f(start) = h(start)

# path(start) = []

# pq.push(start, f(start))

# while not pq.empty():

# top = pq.pop()

# if isGoal(top):

# return f(top), path(top)

# foreach next in succ(top):

# g(next) = g(top) + cost(top, next)

# f(next) = g(next) + h(next)

# path(next) = path(top).append(next)

# pq.push(next, f(next))

Answer 1

import heapq def calculate_distances(graph, starting_vertex): distances = {vertex: float('infinity') for vertex in graph} distances[starting_vertex] = 0 pq = [(0, starting_vertex)] while len(pq) > 0: current_distance, current_vertex = heapq.heappop(pq) # Nodes can get added to the priority queue multiple times. We only # process a vertex the first time we remove it from the priority queue. if current_distance > distances[current_vertex]: continue for neighbor, weight in graph[current_vertex].items(): distance = current_distance + weight # Only consider this new path if it's better than any path we've # already found. if distance < distances[neighbor]: distances[neighbor] = distance heapq.heappush(pq, (distance, neighbor)) return distances example_graph = { 'U': {'V': 2, 'W': 5, 'X': 1}, 'V': {'U': 2, 'X': 2, 'W': 3}, 'W': {'V': 3, 'U': 5, 'X': 3, 'Y': 1, 'Z': 5}, 'X': {'U': 1, 'V': 2, 'W': 3, 'Y': 1}, 'Y': {'X': 1, 'W': 1, 'Z': 1}, 'Z': {'W': 5, 'Y': 1}, } print(calculate_distances(example_graph, 'X')) # => {'U': 1, 'W': 2, 'V': 2, 'Y': 1, 'X': 0, 'Z': 2}