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Better names in ant_colony

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Johann Dreo 2014-03-18 12:03:10 +01:00
commit bb87ac70f5
1 changed files with 20 additions and 21 deletions
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ant_colony.py
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@ -7,16 +7,17 @@ from collections import Counter
import path import path
def log( *args ): def LOG( *args ):
for msg in args: for msg in args:
sys.stderr.write( str(msg) ) sys.stderr.write( str(msg) )
sys.stderr.write(" ") sys.stderr.write(" ")
sys.stderr.flush() sys.stderr.flush()
def logn( *args ): def LOGN( *args ):
log( *args ) LOG( *args )
log("\n") LOG("\n")
def tour(lst): def tour(lst):
# consecutive pairs in lst + last-to-first element # consecutive pairs in lst + last-to-first element
@ -39,7 +40,7 @@ def cost( permutation, cost_func, cities ):
return dist return dist
def initialize_pheromone_matrix( cities, init_value ): def initialize_pheromones( cities, init_value ):
rows = {} rows = {}
for i in cities: for i in cities:
cols = {} cols = {}
@ -51,12 +52,13 @@ def initialize_pheromone_matrix( cities, init_value ):
def choose( cities, last, exclude, pheromones, w_heuristic, w_history, cost_func = graph_distance ): def choose( cities, last, exclude, pheromones, w_heuristic, w_history, cost_func = graph_distance ):
choices = [] choices = []
for city in cities: for current in cities:
if city in exclude: if current in exclude:
# This is faster than "if current not in exclude"
continue continue
c = {"city" : city} c = {"city" : current}
c["history"] = pheromones[last][city] ** w_history c["history"] = pheromones[last][current] ** w_history
c["distance"] = cost_func( last, city, cities ) c["distance"] = cost_func( last, current, cities )
c["heuristic"] = (1.0 / c["distance"]) ** w_heuristic c["heuristic"] = (1.0 / c["distance"]) ** w_heuristic
c["proba"] = c["history"] * c["heuristic"] c["proba"] = c["history"] * c["heuristic"]
choices.append(c) choices.append(c)
@ -116,22 +118,19 @@ def update_local( pheromones, candidate, w_pheromone, init_pheromone ):
pheromones[j][i] = value pheromones[j][i] = value
def random_permutation( cities ): def search( cities, max_iterations, nb_ants, decay, w_heuristic, w_pheromone, w_history, c_greedy, cost_func = graph_distance ):
# like random.shuffle(cities) but on a copy # like random.shuffle(cities) but on a copy
return sorted( cities, key=lambda i: random.random()) best = { "permutation" : sorted( cities, key=lambda i: random.random()) }
def search( max_iterations, nb_ants, decay, w_heuristic, w_pheromone, w_history, c_greedy, cities, cost_func = graph_distance ):
best = { "permutation" : random_permutation(cities) }
best["cost"] = cost( best["permutation"], cost_func, cities ) best["cost"] = cost( best["permutation"], cost_func, cities )
init_pheromone = 1.0 / float(len(cities)) * best["cost"] init_pheromone = 1.0 / float(len(cities)) * best["cost"]
pheromone = initialize_pheromone_matrix( cities, init_pheromone ) pheromone = initialize_pheromones( cities, init_pheromone )
for i in range(max_iterations): for i in range(max_iterations):
log( i ) LOG( i )
solutions = [] solutions = []
for j in range(nb_ants): for j in range(nb_ants):
log( "." ) LOG( "." )
candidate = {} candidate = {}
candidate["permutation"] = walk( cities, pheromone, w_heuristic, w_history, c_greedy, cost_func ) candidate["permutation"] = walk( cities, pheromone, w_heuristic, w_history, c_greedy, cost_func )
candidate["cost"] = cost( candidate["permutation"], cost_func, cities ) candidate["cost"] = cost( candidate["permutation"], cost_func, cities )
@ -139,7 +138,7 @@ def search( max_iterations, nb_ants, decay, w_heuristic, w_pheromone, w_history,
best = candidate best = candidate
update_local( pheromone, candidate, w_pheromone, init_pheromone ) update_local( pheromone, candidate, w_pheromone, init_pheromone )
update_global( pheromone, best, decay ) update_global( pheromone, best, decay )
logn( best["cost"] ) LOGN( best["cost"] )
return best return best
@ -176,5 +175,5 @@ if __name__ == "__main__":
( 2,-2) : [( 2, 0),( 0,-2)], ( 2,-2) : [( 2, 0),( 0,-2)],
} }
best = search( max_it, num_ants, decay, w_heur, w_local_phero, w_history, c_greed, G, cost_func = graph_distance ) best = search( G, max_it, num_ants, decay, w_heur, w_local_phero, w_history, c_greed, cost_func = graph_distance )
print best["cost"], best["permutation"] print best["cost"], best["permutation"]