Created
July 26, 2012 12:39
-
-
Save George1/3181809 to your computer and use it in GitHub Desktop.
This is my Pyhton's solution for the problem Actor Centrality from CS215 Algorithms class on Udacity
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import csv | |
def make_link(G, node1, node2): | |
if node1 not in G: | |
G[node1] = set() | |
G[node1].add(node2) | |
if node2 not in G: | |
G[node2] = set() | |
G[node2].add(node1) | |
return G | |
def read_graph(filename): | |
tsv = csv.reader(open(filename), delimiter='\t') | |
G = {} | |
actors = set() | |
for (actor, movie, year) in tsv: | |
make_link(G, actor, movie + '(' + year + ')') | |
actors.add(actor) | |
return G, actors | |
def encode(actors_graph, actors): | |
dkey = [] | |
new_actors_graph = [None] * len(actors_graph) | |
string_to_index = {} | |
for node in actors_graph: | |
if node not in string_to_index: | |
string_to_index[node] = len(dkey) | |
dkey.append(node) | |
for name in string_to_index: | |
if name in actors: | |
actors.remove(name) | |
actors.add(string_to_index[name]) | |
edges = [] | |
for edge in actors_graph[name]: | |
edges.append(string_to_index[edge]) | |
new_actors_graph[string_to_index[name]] = tuple(edges) | |
return dkey, new_actors_graph | |
def centrality(G, v): | |
total_distance = 0 | |
open_list = [(v, 0)] | |
visited = [False] * len(G) | |
visited[v] = True | |
idx = 0 | |
while idx < len(open_list): | |
current, distance = open_list[idx] | |
total_distance += distance | |
idx += 1 | |
for neighbor in G[current]: | |
if not visited[neighbor]: | |
open_list.append((neighbor, distance+1)) | |
visited[neighbor] = True | |
return total_distance/float(idx) | |
def compute_centralities(G, actors): | |
C = {} | |
for actor in actors: | |
C[actor] = centrality(G, actor) | |
return C | |
actors_graph, actors = read_graph("imdb-1.tsv") | |
decryption_key, actors_graph = encode(actors_graph, actors) | |
centrs = compute_centralities(actors_graph, actors) | |
twentieth_actor = sorted(centrs.items(), key = lambda item: item[1])[19] | |
print "rank 20: %s centrality=%.3f" % (decryption_key[twentieth_actor[0]], twentieth_actor[1]) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment