def generate_final_frontier_for_gale4(problems, algorithms, Configurations, tag=""):
if "GALE4" not in [algorithm.name for algorithm in algorithms]:
return
else:
for problem in problems:
from Graphics.PerformanceMeasures.DataFrame import ProblemFrame
data = ProblemFrame(problem, [a for a in algorithms if a.name == "GALE4"])
# data for all repeats
total_data = [data.get_frontier_values(gen_no) for gen_no in
xrange(Configurations["Universal"]["No_of_Generations"])]
data_for_all_generations = []
for repeat in xrange(Configurations["Universal"]["Repeats"]):
temp_data = []
for gen_no in xrange(Configurations["Universal"]["No_of_Generations"]):
temp_data.extend(total_data[gen_no]["GALE4"][repeat])
from jmoo_individual import jmoo_individual
solutions = [jmoo_individual(problem, td.decisions, problem.evaluate(td.decisions)) for td in temp_data]
# non dominated sorting
from jmoo_algorithms import selNSGA2
final_solutions, _ = selNSGA2(problem, [], solutions, Configurations)
for i in xrange(Configurations["Universal"]["No_of_Generations"]):
filename = "./RawData/PopulationArchives/" + "GALE4" + "_" + problem.name + "/" + str(
repeat) + "/" + \
str(i + 1) + ".txt"
f = open(filename, "w")
for fs in final_solutions:
f.write(','.join([str(fss) for fss in fs.decisionValues]) + "," + ",".join(
[str(fss) for fss in fs.fitness.fitness]) + "\n")
f.close()
def generate_final_frontier_for_gale4(problems,
algorithms,
Configurations,
tag=""):
if "GALE4" not in [algorithm.name for algorithm in algorithms]: return
else:
for problem in problems:
from Graphics.PerformanceMeasures.DataFrame import ProblemFrame
data = ProblemFrame(problem,
[a for a in algorithms if a.name == "GALE4"])
# data for all repeats
total_data = [
data.get_frontier_values(gen_no) for gen_no in xrange(
Configurations["Universal"]["No_of_Generations"])
]
data_for_all_generations = []
for repeat in xrange(Configurations["Universal"]["Repeats"]):
temp_data = []
for gen_no in xrange(
Configurations["Universal"]["No_of_Generations"]):
temp_data.extend(total_data[gen_no]["GALE4"][repeat])
from jmoo_individual import jmoo_individual
solutions = [
jmoo_individual(problem, td.decisions,
problem.evaluate(td.decisions))
for td in temp_data
]
# non dominated sorting
from jmoo_algorithms import selNSGA2
final_solutions, _ = selNSGA2(problem, [], solutions,
Configurations)
for i in xrange(
Configurations["Universal"]["No_of_Generations"]):
filename = "./RawData/PopulationArchives/" + "GALE4" + "_" + problem.name + "/" + str(repeat) + "/" + \
str(i+1) + ".txt"
f = open(filename, "w")
for fs in final_solutions:
f.write(
','.join([str(fss)
for fss in fs.decisionValues]) + "," +
",".join([str(fss)
for fss in fs.fitness.fitness]) + "\n")
f.close()
def compare_methods(problem):
initialPopulation(problem, Configurations["Universal"]["Population_Size"] * 2, "unittesting")
population = problem.loadInitialPopulation(Configurations["Universal"]["Population_Size"] * 2, "unittesting")
population = add_id_to_population(population)
approx, approx_eval = do_fastmap_domination(problem, population)
# non dominated sorting
from jmoo_algorithms import selNSGA2
final_solutions, _ = selNSGA2(problem, [], population, Configurations)
approx_id = [a.id for a in approx]
final_id = [f.id for f in assign_ids(population, final_solutions)]
count = 0
for aid in approx_id:
if aid in final_id: count += 1
# print "Name: ",problem.name, " Match: ", round((count/len(final_id)) * 100, 2), "Approx Evals: ", approx_eval, "Total Evaluation: ", Configurations["Universal"]["Population_Size"] * 2
return round((count/len(final_id)) * 100, 2)
def compare_methods(problem):
initialPopulation(problem, Configurations["Universal"]["Population_Size"] * 2, "unittesting")
population = problem.loadInitialPopulation(Configurations["Universal"]["Population_Size"] * 2, "unittesting")
population = add_id_to_population(population)
approx, approx_eval = do_fastmap_domination(problem, population)
# non dominated sorting
from jmoo_algorithms import selNSGA2
final_solutions, _ = selNSGA2(problem, [], population, Configurations)
approx_id = [a.id for a in approx]
final_id = [f.id for f in assign_ids(population, final_solutions)]
count = 0
for aid in approx_id:
if aid in final_id: count += 1
# print "Name: ",problem.name, " Match: ", round((count/len(final_id)) * 100, 2), "Approx Evals: ", approx_eval, "Total Evaluation: ", Configurations["Universal"]["Population_Size"] * 2
return round((count/len(final_id)) * 100, 2)