def main():
# defining initial population
pop = toolbox.MST_pop(n=nDisks)
# clean up old files
if os.path.exists('./stoppingRate.txt'):
os.remove('stoppingRate.txt')
if os.path.exists('./highEElectrons.txt'):
os.remove('highEElectrons.txt')
# clean up old result directories
shutil.rmtree('runOutput', ignore_errors=True)
print('Cleared run output')
shutil.rmtree('evalOut', ignore_errors=True)
print("Cleared evaluation output")
# making new directories
if not os.path.exists('./runOutput'):
os.mkdir("./runOutput/")
if not os.path.exists('./evalOut'):
os.mkdir("./evalOut")
print("-- Generation 1 --")
# creating SIMG4 directory for the first iteration
simG4Dir = '/vols/comet/users/sy5118/newBuild/OfflineProject/packages/SimG4/geometry_macros/GA_MST/generated_MST1'
if not os.path.exists(simG4Dir):
os.makedirs(simG4Dir)
j = 0
for mst in pop:
baby_MST = MST(nDisksOnAxis, mst, gen=1, id=j)
baby_MST.write_MST(simG4Dir, j)
j = j + 1
fitnesses = list(
map(toolbox.evaluate, np.ones(nDisks), range(1, nDisks + 1)))
storage = np.array([pop[1]])
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
# CXPB is the probability with which two individuals
# are crossed
#
# MUTPB is the probability for mutating an individual
CXPB, MUTPB = 0.5, 0.2
# Extracting all the fitnesses of
fits = [ind.fitness.values[0] for ind in pop]
# counter
g = 1
# Begin the evolution
while max(fits) < 1 and g < 5:
g = g + 1
# A new generation
simG4Dir = '/vols/comet/users/sy5118/newBuild/OfflineProject/packages/SimG4/geometry_macros/GA_MST/generated_MST' + str(
g)
if not os.path.exists(simG4Dir):
os.makedirs(simG4Dir)
print("-- Generation %i --" % g)
# Select the next generation individuals
offspring = toolbox.select(pop, len(pop))
# Apply crossover and mutation on the offspring
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() < CXPB:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
for mutant in offspring:
if random.random() < MUTPB:
toolbox.mutate(mutant)
del mutant.fitness.values
j = 1
for pop in offspring:
baby_MST = MST(nDisksOnAxis, pop, gen=g, id=j)
baby_MST.write_MST(simG4Dir, j)
pop.fitness.values = toolbox.evaluate(g, j)
j = j + 1
# Clone the selected individuals
offspring = list(map(toolbox.clone, offspring))
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
pop = offspring
# Gather all the fitnesses in one list and print the stats
fits = [ind.fitness.values[0] for ind in pop]
from k_means import * from convex_hull import * N = 10000 # points quantity sample_N = 100 # points for testing data K = 2 # clusters amount mass = [] edges = [] mst = [] center = [[130, 125], [700, 300]] # test centers of generated clusters gen = data_gen(center, N, K) # data generator dr = data_drawer(K) ch = convex_hull() m = MST(100, K) #mass = gen.normal_gen() #mass = gen.nested_data() mass = gen.get_random_data() # all points #mass = gen.get_real_random_data() #mass = gen.strip_data() sample = mass[:sample_N] smpl = data_gen(center, sample_N, K) smpl.mass = sample s_edges = smpl.get_edges() s_edges.sort() mst = m.get_MST(s_edges) km_obj = k_means(mass, K) #mass = gen.strip_data() #convex = ch.convex_hull(result[0]) #dr.draw_MST(mst, mass)
