def findGAInRankedBF():
os.chdir(ga_location)
x = np.load('final_GA_results.npz')
ga_results = x['results'].item()
results = {dim: {} for dim in dimensions}
os.chdir(brute_location)
for dim in dimensions:
for fid in functions:
ga = reprToInt(ga_results[dim][fid]['best_result'])
fit = ga_results[dim][fid]['best_fitness'][-1]
bf_results = tdatFileToFitnesses(raw_bfname.format(dim, fid))
bf_results.sort(key=lambda a: a.fitness)
indexes = [reprToInt(a.ES) for a in bf_results]
ga_index = indexes.index(ga)
# Where does the GA-found ERT/FCE result rank in the brute-force results?
fit_index = 0
max_index = len(bf_results)
while fit_index < max_index and fit > bf_results[fit_index].fitness:
fit_index += 1
results[dim][fid] = (ga, fit_index, ga_index, indexes)
print(
"{:>2}D F{:>2}: GA {:>4} is ranked {:>4} ({:>4})\t\t\t GA: {} \t BF[0]: {}"
.format(dim, fid, ga, fit_index, ga_index, fit,
bf_results[0].fitness))
with open('rank_ga_in_bf.dat', 'w') as f:
cPickle.dump(results, f)
def printBFFitDistances():
os.chdir(brute_location)
for dim in dimensions:
for fid in functions:
bf_results = tdatFileToFitnesses(raw_bfname.format(dim, fid))
bf_results.sort(key=lambda a: a.fitness)
print("{:>2}dim F{:>2}: {}".format(
dim, fid, [str(res.fitness) for res in bf_results[::100]]))
def createArffFiles():
os.chdir("/media/Data/Research Data/Evolving ES data")
print(os.getcwd())
for fid in functions:
for dim in dimensions:
tdat_name = raw_bfname.format(dim, fid)
arff_name = "arff/bruteforce_{}_f{}.arff".format(dim, fid)
datToArff(tdat_name, arff_name, dim)
def getBestFromRankedBF(dim, fid, num=10):
os.chdir(brute_location)
bf_results = tdatFileToFitnesses(raw_bfname.format(dim, fid))
bf_results.sort(key=lambda a: a.fitness)
indexes = [reprToInt(a.ES) for a in bf_results]
results = []
for i in range(num):
results.append((indexes[i], i, bf_results[i].fitness))
return results
def findGivenInRankedBF(dim, fid, given):
os.chdir(brute_location)
bf_results = tdatFileToFitnesses(raw_bfname.format(dim, fid))
bf_results.sort(key=lambda a: a.fitness)
indexes = [reprToInt(a.ES) for a in bf_results]
results = []
for ES in given:
index = indexes.index(ES)
results.append(
ranked_ES_and_result(ES, index, bf_results[index].fitness))
return results
def storeBestFromBF():
os.chdir(brute_location)
results = {dim: {} for dim in dimensions}
for dim in dimensions:
for fid in functions:
bf_results = tdatFileToFitnesses(raw_bfname.format(dim, fid))
bf_results.sort(key=lambda a: a.fitness)
results[dim][fid] = bf_results[0]
with open('brute_results.dat', 'w') as f:
cPickle.dump(results, f)
def checkFileSizesBF():
os.chdir(brute_location)
for dim in dimensions:
print(dim)
for fid in functions:
with open(raw_bfname.format(dim, fid)) as f:
lines = [line for line in f]
if len(lines) != 4608:
print(
"File bruteforce_{}_f{}.tdat does not contain 4608 entries! ({})"
.format(dim, fid, len(lines)))