def cross_over_for_genetic(pop, result_cluster, movie_name_arr, movie_had_seen):
payoff_list = []
name_list = []
new_name_list = []
new_payoff_list = []
list_for_cross_use = []
payoff_list, name_list = split_payoff_and_name( pop )
new_pop = []
cross_over_location = random.randint(1,len(name_list)-2)
for i in range( len(name_list) ):
name_coll = name_list[i]
for j in range( len(result_cluster) ):
# use a flag variable to mark if there is a match
flag = 0
for k in result_cluster[j]:
if name_coll == movie_name_arr[k]:
flag = 1
if flag == 1:
for i in range(10000):
rand_loc = random.randint(0, len(result_cluster[j])-1 )
location = result_cluster[j][rand_loc]
if movie_name_arr[location] not in movie_had_seen.keys():
list_for_cross_use.append(movie_name_arr[location])
break
new_name_list = name_list[0:cross_over_location] + list_for_cross_use[cross_over_location:]
new_payoff_list = gt.gen_payoff_list(new_name_list)
new_pop = zip( new_payoff_list, new_name_list )
return new_pop
def cross_over_for_genetic(pop, result_cluster, movie_name_arr,
movie_had_seen):
payoff_list = []
name_list = []
new_name_list = []
new_payoff_list = []
list_for_cross_use = []
payoff_list, name_list = split_payoff_and_name(pop)
new_pop = []
cross_over_location = random.randint(1, len(name_list) - 2)
for i in range(len(name_list)):
name_coll = name_list[i]
for j in range(len(result_cluster)):
# use a flag variable to mark if there is a match
flag = 0
for k in result_cluster[j]:
if name_coll == movie_name_arr[k]:
flag = 1
if flag == 1:
for i in range(10000):
rand_loc = random.randint(0, len(result_cluster[j]) - 1)
location = result_cluster[j][rand_loc]
if movie_name_arr[location] not in movie_had_seen.keys():
list_for_cross_use.append(movie_name_arr[location])
break
new_name_list = name_list[0:cross_over_location] + list_for_cross_use[
cross_over_location:]
new_payoff_list = gt.gen_payoff_list(new_name_list)
new_pop = zip(new_payoff_list, new_name_list)
return new_pop
def mutate_for_genetic( pop, result_cluster, movie_name_arr, movie_had_seen ):
payoff_list = []
name_list = []
new_name_list = []
new_payoff_list = []
payoff_list, name_list = split_payoff_and_name( pop )
new_pop = []
mutate_location = random.randint( 0, len(name_list)-1 )
name = name_list[mutate_location]
for j in range( len(result_cluster) ):
# use a flag variable to mark if there is a match
flag = 0
for k in result_cluster[j]:
if name == movie_name_arr[k]:
flag = 1
if flag == 1:
for i in range(10000):
rand_loc = random.randint(0, len(result_cluster[j])-1 )
location = result_cluster[j][rand_loc]
if movie_name_arr[location] not in movie_had_seen.keys():
new_name_list = name_list[0:mutate_location] + [movie_name_arr[location]] + \
name_list[mutate_location+1:]
break
new_payoff_list = gt.gen_payoff_list(new_name_list)
new_pop = zip(new_payoff_list, new_name_list)
return new_pop
def mutate_for_genetic(pop, result_cluster, movie_name_arr, movie_had_seen):
payoff_list = []
name_list = []
new_name_list = []
new_payoff_list = []
payoff_list, name_list = split_payoff_and_name(pop)
new_pop = []
mutate_location = random.randint(0, len(name_list) - 1)
name = name_list[mutate_location]
for j in range(len(result_cluster)):
# use a flag variable to mark if there is a match
flag = 0
for k in result_cluster[j]:
if name == movie_name_arr[k]:
flag = 1
if flag == 1:
for i in range(10000):
rand_loc = random.randint(0, len(result_cluster[j]) - 1)
location = result_cluster[j][rand_loc]
if movie_name_arr[location] not in movie_had_seen.keys():
new_name_list = name_list[0:mutate_location] + [movie_name_arr[location]] + \
name_list[mutate_location+1:]
break
new_payoff_list = gt.gen_payoff_list(new_name_list)
new_pop = zip(new_payoff_list, new_name_list)
return new_pop
