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""" Depth-first search (recursive and iterative solutions)
This ignores the distance between the city data, and just cares about number of hops.
This implementation is admittedly tightly-coupled to my particular city_data data set
(to add clarity in understanding by making it tied to concrete data).
The city_data is a list of 28 large cities in the United States, with some considered
neighbors of the others. This would be the case in, say, a bus system which only
travels between major cities. Using the shortest path on this data outputs
the best path through these major cities.
The format of city_data is like this:
{ "Milwaukee, WI": {"Minneapolis, MN": 542093, "Chicago, IL": 148198},
"Minneapolis, MN": {"Seattle, WA": 2665735, "Milwaukee, WI": 541660}, ... }
So the neighbors of a city node can be found like this: list(city_data["Milwaukee, WI"].keys())
"""
import json
import sys
def unroll_shortest_path(current, optimal_parent_map, path=()):
if current is None: # Reached the start node
return path
else:
return unroll_shortest_path(optimal_parent_map[current], optimal_parent_map, (current,) + path)
def depth_first_search_iter(from_city, to_city, city_data):
to_visit = [from_city]
visited = set([from_city])
parent_map = {from_city: None}
while to_visit != []:
current = to_visit.pop()
visited.add(current)
neighbors = city_data[current].keys()
for n in neighbors:
if n == to_city:
parent_map[n] = current
return unroll_shortest_path(to_city, parent_map)
elif n not in visited:
parent_map[n] = current
to_visit.append(n)
return None
def depth_first_search(from_city, to_city, city_data):
visited = set()
def _depth_first_search(from_city, to_city, city_data, path=(from_city,)):
# If destination found, return the path hereto compiled
if from_city == to_city:
return path
else:
visited.add(from_city)
neighbors = city_data[from_city].keys()
unvisited_neighbors = [n for n in neighbors if n not in visited]
# If there are no more unvisited neighbors, this path doesn't lead to the goal
if unvisited_neighbors == []:
return None
# Else, for each unvisited neighbor, recursively try to reach the solution
# from it as the "from_city". Also update the path
for n in unvisited_neighbors:
result = _depth_first_search(n, to_city, city_data, path+(n,))
if result is not None:
return result
return _depth_first_search(from_city, to_city, city_data)
def get_city_data():
city_data = None
with open("city_data.json","r") as f:
city_data = json.loads(f.read())
return city_data
if __name__ == '__main__':
city_data = get_city_data()
try:
city_from = sys.argv[1]
city_to = sys.argv[2]
except IndexError:
print("Usage:", sys.argv[0], "\"from city\" \"to city>\"")
print("City choices:")
for city in city_data:
print(" -", city)
sys.exit(1)
print("Recursive:", depth_first_search(city_from, city_to, city_data))
print("\nIterative:", depth_first_search_iter(city_from, city_to, city_data))
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