def TTCvsMMMD(gridPoint): if not (gridPoint['compositeClass0'] == 'integerVectorSymmetricRangeMutations' and gridPoint['probabilityOfMutatingClass0'] == 0.2): return # determining the time of convergence bilogFileName = 'bestIndividual{}.log'.format(gridPoint['randomSeed']) bilog = np.loadtxt(bilogFileName) toc = None # time of convergence # TOME OF CONVERGENCE for i in range(bilog.shape[0]): if -1. * bilog[i, 1] <= fitnessThreshold: toc = i break if toc is None: with open('../results/nonconverged runs', 'a') as ncrf: ncrf.write(str(gridPoint['randomSeed']) + '\n') return # making a scatter of points of TTC vs MMMD with open('../results/ttcvsmmmd', 'a') as tmfile: for i in range(evsDefaults['genStopAfter'] + 1): tmfile.write('{} {}\n'.format( toc - i, gctools.minParetoFrontHammingDistanceToMMM(i)))
def generateMinMMMDistTimeSeries(gridPoint): minMMMDistTS = [ gctools.minParetoFrontHammingDistanceToMMM(gen) for gen in range(1, evsDefaults['genStopAfter'] + 1) ] filename = minMMMDistFileName(gridPoint) with open(filename, 'a') as file: file.write(' '.join(map(str, minMMMDistTS)) + '\n')
def generateMinMMMDistTimeSlices(gridPoint): return [ gctools.minParetoFrontHammingDistanceToMMM(gen) for gen in stages ]