def mousePressEvent(self, event):
global selected_spring, focus_spring, selected_springbot
global CORES
# A principio libera qualquer spring e springbot
selected_spring = None
# verifica se selecionou algum node ou spring
if self.focus_node:
if mainwindow.edit_mode and event.button() == QtCore.Qt.MidButton:
self.focus_node.parent.remove(self.focus_node)
# Deleta todos nodes nao conectados
for node in self.focus_node.parent.unconnected():
self.focus_node.parent.remove(node)
# Verifica se matou springbot
if len(self.focus_node.parent.nodes) == 0:
mainwindow.springbots.remove(self.focus_node.parent)
if self.focus_node.parent is selected_springbot:
selected_springbot = random.choice(
mainwindow.springbots
) if mainwindow.springbots else None
self.focus_node = None
else:
self.selected_node = self.focus_node
selected_springbot = self.focus_node.parent
elif focus_spring:
if mainwindow.edit_mode and event.button() == QtCore.Qt.MidButton:
focus_spring.parent.remove(focus_spring)
# Deleta todos nodes nao conectados
for node in focus_spring.parent.unconnected():
focus_spring.parent.remove(node)
focus_spring = None
else:
selected_spring = focus_spring
selected_springbot = focus_spring.parent
elif mainwindow.edit_mode and event.button() == QtCore.Qt.RightButton:
# cria nodo(novo springbot)
newspringbot = EvolveSpringbot(name=latimname(5))
newspringbot.cor = random.choice(CORES)
self.selected_node = self.focus_node = Node(pos=(event.x(),
event.y()))
newspringbot.add(self.focus_node)
mainwindow.springbots.append(newspringbot)
selected_springbot = newspringbot
self.creating = (self.selected_node
and event.button() == QtCore.Qt.RightButton)
self.mouse_pos = (event.x(), event.y())
def mousePressEvent (self, event):
global selected_spring, focus_spring, selected_springbot
global CORES
# A principio libera qualquer spring e springbot
selected_spring = None
# verifica se selecionou algum node ou spring
if self.focus_node:
if mainwindow.edit_mode and event.button() == QtCore.Qt.MidButton:
self.focus_node.parent.remove(self.focus_node)
# Deleta todos nodes nao conectados
for node in self.focus_node.parent.unconnected():
self.focus_node.parent.remove(node)
# Verifica se matou springbot
if len(self.focus_node.parent.nodes) == 0:
mainwindow.springbots.remove(self.focus_node.parent)
if self.focus_node.parent is selected_springbot:
selected_springbot = random.choice(mainwindow.springbots) if mainwindow.springbots else None
self.focus_node = None
else:
self.selected_node = self.focus_node
selected_springbot = self.focus_node.parent
elif focus_spring:
if mainwindow.edit_mode and event.button() == QtCore.Qt.MidButton:
focus_spring.parent.remove(focus_spring)
# Deleta todos nodes nao conectados
for node in focus_spring.parent.unconnected():
focus_spring.parent.remove(node)
focus_spring = None
else:
selected_spring = focus_spring
selected_springbot = focus_spring.parent
elif mainwindow.edit_mode and event.button() == QtCore.Qt.RightButton:
# cria nodo(novo springbot)
newspringbot = EvolveSpringbot(name = latimname(5))
newspringbot.cor = random.choice(CORES)
self.selected_node = self.focus_node = Node(pos=(event.x(), event.y()))
newspringbot.add(self.focus_node)
mainwindow.springbots.append(newspringbot)
selected_springbot = newspringbot
self.creating = (self.selected_node and event.button() == QtCore.Qt.RightButton)
self.mouse_pos = (event.x(), event.y())
help="Fitness function used to evolve, default is walk",
metavar="FITNESS",
)
(options, args) = parser.parse_args()
if not HAS_PYGAME:
options.graphics = False
# Load fitness function
fitness = fitness.__dict__[options.fitness]
options.save_freq = int(options.save_freq)
options.limit = int(options.limit)
options.start_at = int(options.start_at)
options.prefix = options.prefix if options.prefix is not None else lower(latimname(3))
if options.verbose:
print "# %s experiment." % options.prefix
if len(args) == 0:
readfile = sys.stdin
else:
readfile = args[0]
# Reads the initial population
init_population = load_xml(readfile)
# If graphics is enabled, starts pygame
if options.graphics:
pygame.init()
pygame.display.set_mode(
def serial_evolve(
population,
fitness=fitness.walk,
save_freq=100,
limit=-1,
verbose=False,
graphics=False,
discard_fraction=0.4,
random_insert=0.1,
best=False,
start_iteration=1,
prefix="",
):
"""
Given the initial population 'population', executes
a genetic algorithm to evolve them for best fitness.
Saves the population each 'save_freq' interval(ordered by fitness)
"""
# Test if parameters are correct
if discard_fraction < 0 or random_insert < 0:
raise ValueError("discard_fraction and random_insert must both range from 0 to 1")
elif discard_fraction + random_insert > 1:
raise ValueError("the sum of discard_fraction and random_insert must not be greater than 1")
iter = start_iteration # Initial iteration
# Calculate amount of discarded and random population
discarded = int(len(population) / 2 * discard_fraction)
randoms = int(len(population) / 2 * random_insert)
if verbose:
print "# Initiating simulation with a population of %d specimens." % (len(population))
print "# Evolving for %s:" % (fitness.__name__)
print "# At each iteration %d will be discarded, %d of the remaining will" % (discarded, discarded - randoms),
print " be selected cloned and mutated and %d random springbots will be inserted" % (randoms)
# Transforms population into evolvespringbots
population = [EvolveSpringbot(springbot) for springbot in population]
try:
while population and iter != limit:
if verbose:
print "Iteration %d:" % (iter)
z = 1
fitness_sum = 0
bloodline_len_sum = 0
# Tests fitness for each springbot
for specimen in population:
specimen["fitness"] = fitness(specimen, WIDTH, HEIGHT, graphics)
if verbose:
print '\t%d/%d: "%s"(%d) %.3f' % (
z,
len(population),
specimen["name"],
specimen.generations(),
specimen["fitness"],
)
z += 1
bloodline_len_sum += specimen.generations()
fitness_sum += specimen["fitness"]
if verbose:
print "Bloodline lenght average: %.4f" % (bloodline_len_sum / float(len(population)))
print "Fitness average: %.4f" % (fitness_sum / float(len(population)))
# Now Order population by its fitness
population.sort(cmp=lambda A, B: cmp(A["fitness"], B["fitness"]), reverse=True)
# Discards some of the worse half
for specimen in sample(population[len(population) / 2 :], discarded + randoms):
population.remove(specimen)
# Clones and mutates some of the remaining
for specimen in sample(population, discarded):
child = EvolveSpringbot(specimen).mutate()
child.addBloodline(specimen)
names = child["name"].split()
# Gives a child's name
if len(names) == 1:
child["name"] = names[0] + " " + latimname(2)
elif len(names) == 2:
child["name"] = names[0] + " " + names[1] + " " + latimname(2)
elif len(names) == 3:
child["name"] = names[0] + " " + names[2] + " " + latimname(2)
# Incorporate children into the population
population.append(child)
# Incorporate randoms
population += [EvolveSpringbot(random=True) for x in xrange(randoms)]
# Test if it is time to save population
if iter % save_freq == 0:
# Saves the current population
filename = "%s-%s-p%d-i%d.xml" % (prefix, fitness.__name__, len(population), iter)
store_xml(population, filename)
if verbose:
print "# iteration %d saved into %s" % (iter, filename)
# Saves best if asked
if best:
filename = "%s-%s-p%d-best.xml" % (prefix, fitness.__name__, len(population))
store_xml(population[:1], filename)
if verbose:
print "# Best of iteration %d saved into %s" % (iter, filename)
# Increments iteration
iter += 1
except KeyboardInterrupt:
pass
# Order population by its fitness
population.sort(reverse=True)
# Now, saves the current population and quit
filename = "%s-%s-p%d-i%d.xml" % (prefix, fitness.__name__, len(population), iter)
store_xml(population, filename)
if verbose:
print
print "# iteration %d saved into %s" % (iter, filename)
print "# terminating..."
def serial_evolve(population, fitness=fitness.walk, save_freq=100,
limit=-1,
verbose=False, graphics=False, discard_fraction=0.4, random_insert=0.1,
best=False, start_iteration = 1, prefix=''):
"""
Given the initial population 'population', executes
a genetic algorithm to evolve them for best fitness.
Saves the population each 'save_freq' interval(ordered by fitness)
"""
# Test if parameters are correct
if discard_fraction < 0 or random_insert < 0:
raise ValueError("discard_fraction and random_insert must both range from 0 to 1")
elif discard_fraction + random_insert > 1:
raise ValueError("the sum of discard_fraction and random_insert must not be greater than 1")
iter = start_iteration # Initial iteration
# Calculate amount of discarded and random population
discarded = int(len(population)/2 * discard_fraction)
randoms = int(len(population)/2 * random_insert)
if verbose:
print("# Initiating simulation with a population of %d specimens." % (len(population)))
print("# Evolving for %s:" % (fitness.__name__))
print("# At each iteration %d will be discarded, %d of the remaining will" % (discarded, discarded-randoms), end=' ')
print(" be selected cloned and mutated and %d random springbots will be inserted" % (randoms))
# Transforms population into evolvespringbots
population = [EvolveSpringbot(springbot) for springbot in population]
try:
while population and iter != limit:
if verbose:
print("Iteration %d:" % (iter))
z = 1
fitness_sum = 0
bloodline_len_sum = 0
# Tests fitness for each springbot
for specimen in population:
specimen['fitness'] = fitness(specimen, WIDTH, HEIGHT, graphics)
if verbose:
print("\t%d/%d: \"%s\"(%d) %.3f" % \
(z, len(population), specimen['name'],
specimen.generations(), specimen['fitness']))
z += 1
bloodline_len_sum += specimen.generations()
fitness_sum += specimen['fitness']
if verbose:
print("Bloodline lenght average: %.4f" % (bloodline_len_sum/float(len(population))))
print("Fitness average: %.4f" % (fitness_sum/float(len(population))))
# Now Order population by its fitness
population.sort(
cmp=lambda A,B: cmp(A['fitness'], B['fitness']), reverse=True)
# Discards some of the worse half
for specimen in sample(population[len(population)//2:], discarded + randoms):
population.remove(specimen)
# Clones and mutates some of the remaining
for specimen in sample(population, discarded):
child = EvolveSpringbot(specimen).mutate()
child.addBloodline(specimen)
names = child['name'].split()
# Gives a child's name
if len(names) == 1:
child['name'] = names[0] + " " + latimname(2)
elif len(names) == 2:
child['name'] = names[0] + " " + names[1] + " " + latimname(2)
elif len(names) == 3:
child['name'] = names[0] + " " + names[2] + " " + latimname(2)
# Incorporate children into the population
population.append(child)
# Incorporate randoms
population += [EvolveSpringbot(random=True) for x in range(randoms)]
# Test if it is time to save population
if iter % save_freq == 0:
# Saves the current population
filename = "%s-%s-p%d-i%d.xml" % (prefix, fitness.__name__, len(population), iter)
store_xml(population, filename)
if verbose:
print("# iteration %d saved into %s" % (iter, filename))
# Saves best if asked
if best:
filename = "%s-%s-p%d-best.xml" % (prefix, fitness.__name__, len(population))
store_xml(population[:1], filename)
if verbose:
print("# Best of iteration %d saved into %s" % (iter, filename))
# Increments iteration
iter += 1
except KeyboardInterrupt:
pass
# Order population by its fitness
population.sort(
cmp=lambda A,B: cmp(A['fitness'], B['fitness']), reverse=True)
# Now, saves the current population and quit
filename = "%s-%s-p%d-i%d.xml" % (prefix, fitness.__name__, len(population), iter)
store_xml(population, filename)
if verbose:
print()
print("# iteration %d saved into %s" % (iter, filename))
print("# terminating...")
help="Append a prefix to population file names saved, default is a random name", metavar="PREFIX")
parser.add_option("-f", "--fitness", dest="fitness", default="walk",
help="Fitness function used to evolve, default is walk", metavar="FITNESS")
(options, args) = parser.parse_args()
if not HAS_PYGAME:
options.graphics = False
# Load fitness function
fitness = fitness.__dict__[options.fitness]
options.save_freq = int(options.save_freq)
options.limit = int(options.limit)
options.start_at = int(options.start_at)
options.prefix = options.prefix if options.prefix is not None else latimname(3).lower()
if options.verbose: print("# %s experiment." % options.prefix)
if len(args) == 0:
readfile = sys.stdin
else:
readfile = args[0]
# Reads the initial population
init_population = load_xml(readfile)
# If graphics is enabled, starts pygame
if options.graphics:
pygame.init()
pygame.display.set_mode((WIDTH,HEIGHT),
pygame.FULLSCREEN | pygame.DOUBLEBUF | pygame.HWSURFACE if options.fullscreen else pygame.DOUBLEBUF)
def network_evolve(save_freq=100,
limit=-1,
verbose=False,
discard_fraction=0.4,
random_insert=0.1,
best=False,
start_iteration=1,
prefix=''):
"""
Given the initial population 'population', executes
a genetic algorithm to evolve them for best fitness.
Saves the population each 'save_freq' interval(ordered by fitness)
"""
global population, servers, fitness_function
# Test if parameters are correct
if discard_fraction < 0 or random_insert < 0:
raise ValueError(
"discard_fraction and random_insert must both range from 0 to 1")
elif discard_fraction + random_insert > 1:
raise ValueError(
"the sum of discard_fraction and random_insert must not be greater than 1"
)
iteration = start_iteration # Initial iteration
# Calculate amount of discarded and random population
discarded = int(len(population) / 2 * discard_fraction)
randoms = int(len(population) / 2 * random_insert)
if verbose:
print("# Initiating simulation with a population of %d specimens..." %
(len(population)))
print("# Evolving for %s:" % (fitness_function))
print(
"# At each iteration %d will be discarded, %d of the remaining will"
% (discarded, discarded - randoms),
end=' ')
print(
" be selected cloned and mutated and %d random springbots will be inserted"
% (randoms))
# Turn all population into NetworkEvolveSpringbot
population = [
NetworkEvolveSpringbot(springbot) for springbot in population
]
threads = []
try:
while population and iteration != limit:
# (Re)load servers from file
load_servers()
if verbose:
print("Iteration %d:" % (iteration))
fitness_sum = 0
bloodline_len_sum = 0
# Create threads
threads = [FitnessThread(i) for i in range(len(population))]
# Start all threads
for thread in threads:
thread.start()
# Join(waits) all threads
for thread in threads:
thread.join()
if verbose:
specimen = population[thread.index]
print("\t%d/%d: \"%s\"(%d) %.3f" % \
(thread.index+1, len(population), specimen['name'],
specimen.generations(), specimen['fitness']))
bloodline_len_sum += specimen.generations()
fitness_sum += specimen['fitness']
if verbose:
print("Bloodline lenght average: %.4f" %
(bloodline_len_sum / float(len(population))))
print("Fitness average: %.4f" %
(fitness_sum / float(len(population))))
# Now Order population by its fitness
population.sort(cmp=lambda A, B: cmp(A['fitness'], B['fitness']),
reverse=True)
# Discards some of the worse half
for specimen in sample(population[len(population) // 2:],
discarded + randoms):
population.remove(specimen)
# Clones and mutates some of the remaining half
for specimen in sample(population, discarded):
child = NetworkEvolveSpringbot(specimen).mutate()
child.addBloodline(specimen)
names = child['name'].split()
# Gives a child's name
if len(names) == 1:
child['name'] = names[0] + " " + latimname(2)
elif len(names) == 2:
child['name'] = " ".join(
[names[0], names[1], latimname(2)])
elif len(names) == 3:
child['name'] = " ".join(
[names[0], names[2], latimname(2)])
# Incorporate children into the population
population.append(child)
# Incorporate randoms
population += [
NetworkEvolveSpringbot(random=True) for x in range(randoms)
]
# Test if it is time to save population
if iteration % save_freq == 0:
# Saves the current population
filename = "%s-%s-p%d-i%d.xml" % (prefix, fitness_function,
len(population), iteration)
store_xml(population, filename)
if verbose:
print("# iteration %d saved into %s" %
(iteration, filename))
# Saves best if asked
if best:
filename = "%s-%s-p%d-best.xml" % (prefix, fitness_function,
len(population))
store_xml(population[:1], filename)
if verbose:
print("# Best of iteration %d saved into %s" %
(iteration, filename))
# Increments iteration
iteration += 1
except KeyboardInterrupt:
pass
if verbose:
print("# waiting for threads...")
# Join(waits) all threads
for thread in threads:
thread.join()
# Order population by its fitness
population.sort(reverse=True)
# Now, saves the current population and quit
filename = "%s-%s-p%d-i%d.xml" % (prefix, fitness_function,
len(population), iteration)
store_xml(population, filename)
if verbose:
print()
print("# iteration %d saved into %s" % (iteration, filename))
print("# terminating...")
default=None,
help=
"Append a prefix to population file names saved, default is a random name",
metavar="PREFIX")
(options, args) = parser.parse_args()
# Read command line parameters
serverslist = options.serverslist
fitness_function = options.fitness
options.save_freq = int(options.save_freq)
options.limit = int(options.limit)
options.start_at = int(options.start_at)
options.prefix = options.prefix if options.prefix is not None else lower(
latimname(3))
if options.verbose: print("# %s experiment." % options.prefix)
if len(args) == 0:
readfile = sys.stdin
else:
readfile = args[0]
# Reads the initial population
population = load_xml(readfile)
# Starts the simulation
network_evolve(save_freq=options.save_freq,
limit=options.limit,
verbose=options.verbose,
best=options.best,
def network_evolve(save_freq=100, limit=-1,
verbose=False, discard_fraction=0.4, random_insert=0.1,
best=False, start_iteration = 1, prefix=''):
"""
Given the initial population 'population', executes
a genetic algorithm to evolve them for best fitness.
Saves the population each 'save_freq' interval(ordered by fitness)
"""
global population, servers, fitness_function
# Test if parameters are correct
if discard_fraction < 0 or random_insert < 0:
raise ValueError("discard_fraction and random_insert must both range from 0 to 1")
elif discard_fraction + random_insert > 1:
raise ValueError("the sum of discard_fraction and random_insert must not be greater than 1")
iteration = start_iteration # Initial iteration
# Calculate amount of discarded and random population
discarded = int(len(population)/2 * discard_fraction)
randoms = int(len(population)/2 * random_insert)
if verbose:
print "# Initiating simulation with a population of %d specimens..." % (len(population))
print "# Evolving for %s:" % (fitness_function)
print "# At each iteration %d will be discarded, %d of the remaining will" % (
discarded, discarded-randoms),
print " be selected cloned and mutated and %d random springbots will be inserted" % (
randoms)
# Turn all population into NetworkEvolveSpringbot
population = [NetworkEvolveSpringbot(springbot) for springbot in population]
threads = []
try:
while population and iteration != limit:
# (Re)load servers from file
load_servers()
if verbose:
print "Iteration %d:" % (iteration)
fitness_sum = 0
bloodline_len_sum = 0
# Create threads
threads = [FitnessThread(i) for i in xrange(len(population))]
# Start all threads
for thread in threads:
thread.start()
# Join(waits) all threads
for thread in threads:
thread.join()
if verbose:
specimen = population[thread.index]
print "\t%d/%d: \"%s\"(%d) %.3f" % \
(thread.index+1, len(population), specimen['name'],
specimen.generations(), specimen['fitness'])
bloodline_len_sum +=specimen.generations()
fitness_sum += specimen['fitness']
if verbose:
print "Bloodline lenght average: %.4f" % (bloodline_len_sum/float(len(population)))
print "Fitness average: %.4f" % (fitness_sum/float(len(population)))
# Now Order population by its fitness
population.sort(
cmp=lambda A,B: cmp(A['fitness'], B['fitness']), reverse=True)
# Discards some of the worse half
for specimen in sample(population[len(population)/2:], discarded + randoms):
population.remove(specimen)
# Clones and mutates some of the remaining half
for specimen in sample(population, discarded):
child = NetworkEvolveSpringbot(specimen).mutate()
child.addBloodline(specimen)
names = child['name'].split()
# Gives a child's name
if len(names) == 1:
child['name'] = names[0] + " " + latimname(2)
elif len(names) == 2:
child['name'] = " ".join([names[0], names[1], latimname(2)])
elif len(names) == 3:
child['name'] = " ".join([names[0], names[2], latimname(2)])
# Incorporate children into the population
population.append(child)
# Incorporate randoms
population += [NetworkEvolveSpringbot(random=True) for x in xrange(randoms)]
# Test if it is time to save population
if iteration % save_freq == 0:
# Saves the current population
filename = "%s-%s-p%d-i%d.xml" % (prefix, fitness_function, len(population), iteration)
store_xml(population, filename)
if verbose:
print "# iteration %d saved into %s" % (iteration, filename)
# Saves best if asked
if best:
filename = "%s-%s-p%d-best.xml" % (prefix, fitness_function, len(population))
store_xml(population[:1], filename)
if verbose:
print "# Best of iteration %d saved into %s" % (iteration, filename)
# Increments iteration
iteration += 1
except KeyboardInterrupt:
pass
if verbose:
print "# waiting for threads..."
# Join(waits) all threads
for thread in threads:
thread.join()
# Order population by its fitness
population.sort(reverse=True)
# Now, saves the current population and quit
filename = "%s-%s-p%d-i%d.xml" % (prefix, fitness_function, len(population), iteration)
store_xml(population, filename)
if verbose:
print
print "# iteration %d saved into %s" % (iteration, filename)
print "# terminating..."