def annealing_optimize(result_from_coll_filter, movie_had_seen,
result_from_cluster, movie_name_arr):
temperature = 5000.0
cool = 0.9
# generate the first pop and add to the pop list
sol = []
sol_tmp = []
# create the first pop
payoff_list_coll, name_list_coll = gt.gen_payoff_coll_filt(
result_from_coll_filter)
sol = zip(payoff_list_coll, name_list_coll)
# mian loop
while temperature > 0.1:
payoff_list_tmp, name_list_tmp = gt.gen_payoff_cluster(
result_from_cluster, movie_name_arr, name_list_coll,
movie_had_seen)
sol_tmp = zip(payoff_list_tmp, name_list_tmp)
sol = annealing_choice(sol, sol_tmp, temperature)
# decrease the temperature
temperature = temperature * cool
sol.sort()
sol.reverse()
payoff_list_from_annealing, name_list_from_annealing = split_payoff_and_name(
sol)
return name_list_from_annealing, payoff_list_from_annealing
def annealing_optimize( result_from_coll_filter, movie_had_seen, result_from_cluster, movie_name_arr ):
temperature = 5000.0
cool = 0.96
# generate the first pop and add to the pop list
sol = []
sol_tmp = []
# create the first pop
payoff_list_coll, name_list_coll = gt.gen_payoff_coll_filt(result_from_coll_filter)
sol = zip(payoff_list_coll, name_list_coll)
# mian loop
while temperature > 0.1:
payoff_list_tmp, name_list_tmp = gt.gen_payoff_cluster(result_from_cluster,
movie_name_arr, name_list_coll,
movie_had_seen)
sol_tmp = zip(payoff_list_tmp, name_list_tmp)
sol = annealing_choice( sol, sol_tmp, temperature)
# decrease the temperature
temperature =temperature * cool
sol.sort()
sol.reverse()
payoff_list_from_annealing, name_list_from_annealing = split_payoff_and_name( sol )
return name_list_from_annealing, payoff_list_from_annealing
def genetic_opt(result_from_coll_filter, movie_had_seen, result_from_cluster,
movie_name_arr):
# generate the first pop and add to the pop list
pop = []
# pop_list is a list whose element is some lists like [(a,b),(c,d)]
pop_list = []
elite_rate = 0.2
iteration = 100
mutate_prob = 0.2
payoff_list, name_list = gt.gen_payoff_coll_filt(result_from_coll_filter)
pop = zip(payoff_list, name_list)
pop_list.append(pop)
# define pop size
pop_size = 50
while len(pop_list) < pop_size:
payoff_list, name_list = gt.gen_payoff_cluster(result_from_cluster,
movie_name_arr,
name_list,
movie_had_seen)
pop = zip(payoff_list, name_list)
pop_list.append(pop)
# define pop top elite number
top_elite_num = int(elite_rate * pop_size)
for i in range(iteration):
pop_element_costs = [(payoff_func(j), j) for j in pop_list]
pop_element_costs.sort(cmp=None, key=None, reverse=False)
pop_element_costs.reverse()
ranked_pops = [v for (s, v) in pop_element_costs]
pop_list = ranked_pops[0:top_elite_num]
while len(pop_list) < pop_size:
if random.random() < mutate_prob:
one_in_pop_elite = random.randint(0, top_elite_num)
new_from_mutate = mutate_for_genetic(
ranked_pops[one_in_pop_elite], result_from_cluster,
movie_name_arr, movie_had_seen)
if new_from_mutate != None:
pop_list.append(new_from_mutate)
else:
one_in_pop_elite = random.randint(0, top_elite_num)
new_from_cross_over = cross_over_for_genetic(
ranked_pops[one_in_pop_elite], result_from_cluster,
movie_name_arr, movie_had_seen)
if new_from_cross_over != None:
pop_list.append(new_from_cross_over)
name_list_from_genetic, payoff_list_from_genetic = split_payoff_and_name(
pop_list[0])
return name_list_from_genetic, pop_element_costs[0][0]
def genetic_opt(result_from_coll_filter, movie_had_seen, result_from_cluster, movie_name_arr):
# generate the first pop and add to the pop list
pop = []
# pop_list is a list whose element is some lists like [(a,b),(c,d)]
pop_list = []
elite_rate = 0.2
iteration = 100
mutate_prob = 0.2
payoff_list, name_list = gt.gen_payoff_coll_filt(result_from_coll_filter)
pop = zip(payoff_list, name_list)
pop_list.append(pop)
# define pop size
pop_size = 50
while len(pop_list) < pop_size:
payoff_list, name_list = gt.gen_payoff_cluster(result_from_cluster,
movie_name_arr, name_list, movie_had_seen)
pop = zip(payoff_list, name_list)
pop_list.append(pop)
# define pop top elite number
top_elite_num = int(elite_rate * pop_size)
for i in range( iteration ):
pop_element_costs = [(payoff_func(j),j) for j in pop_list]
pop_element_costs.sort(cmp=None, key=None, reverse=False)
pop_element_costs.reverse()
ranked_pops = [ v for (s,v) in pop_element_costs]
pop_list = ranked_pops[0:top_elite_num]
while len(pop_list) < pop_size:
if random.random() < mutate_prob:
one_in_pop_elite = random.randint(0, top_elite_num)
new_from_mutate = mutate_for_genetic( ranked_pops[one_in_pop_elite],
result_from_cluster, movie_name_arr,
movie_had_seen)
if new_from_mutate != None:
pop_list.append(new_from_mutate)
else:
one_in_pop_elite = random.randint(0, top_elite_num)
new_from_cross_over = cross_over_for_genetic( ranked_pops[one_in_pop_elite],
result_from_cluster, movie_name_arr,
movie_had_seen )
if new_from_cross_over != None:
pop_list.append(new_from_cross_over)
name_list_from_genetic, payoff_list_from_genetic = split_payoff_and_name( pop_list[0] )
return name_list_from_genetic, pop_element_costs[0][0]