Breadth-first search

Author: calebmadrigal

breadth_first_search.py

@@ -0,0 +1,76 @@
+""" Breadth-first search
+
+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
+from collections import deque
+
+
+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 breadth_first_search(from_city, to_city, city_data):
+    to_visit = deque([from_city])
+    visited = set([from_city])
+    parent_map = {from_city: None}
+
+    while to_visit != []:
+        current = to_visit.popleft()  # Treat to_visit as queue
+        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 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(breadth_first_search(city_from, city_to, city_data))
+

depth_first_search.py

@@ -1,4 +1,4 @@
-""" Breadth-first search
+""" Depth-first search (recursive and iterative solutions)
 
 This ignores the distance between the city data, and just cares about number of hops.