def initialize(self, reseed=True):
b = timer()
self.test_settings(self.settings)
self.gen = 0
sd = self.settings['rand_seed']
if reseed:
prng.seed(sd)
self.settings['seed_used'] = sd
self._print('initializing... seed = %d' % self.settings['seed_used'])
self.crossover = self.pop.model_genome.crossover # get the crossover op from the first genome
self.pop.settings = self.settings #should these be shared?
self.size_pop(self.settings['pop_size'])
self.settings['crossover'] = str(self.crossover).split()[0][1:]
self.settings['selector'] = str(self.pop.selector).split()[0][1:]
self.settings['scaler'] = str(self.pop.scaler).split()[0][1:]
self.settings['genome_type'] = str(
self.pop.model_genome).split()[0][1:]
self.pop.initialize(self.settings)
self.stats = {
'selections': 0,
'crossovers': 0,
'mutations': 0,
'replacements': 0,
'pop_evals': 1,
'ind_evals': 0
}
self.stats.update(self.pop.stats)
self.step_time = timer() - b
self.init_dbase()
def initialize(self, mode='serial', reseed=True):
b = timer()
#same as galg
self.test_settings(self.settings)
self.gen = 0
sd = self.settings['rand_seed']
if reseed:
prng.seed(sd)
self.settings['seed_used'] = sd
self._print('initializing... seed = %d' % self.settings['seed_used'])
self.crossover = self.pop[
0].crossover # get the crossover op from the first genome
self.pop.settings = self.settings
#end same as galg
#set up my population to hold the best from each sub-pop
self.pop._size(0) #erase any current member of the pop
self.pop._size(self.settings['num_pops'])
self.crossover = self.pop[0].crossover
#extract the galg settings so we don't get a ton of warnings
#and create the sub ga_s
sub_ga_settings = {}
self.GAs = []
for key in galg.valid_settings:
sub_ga_settings[key] = self.settings[key]
for i in range(self.settings['num_pops']):
self.GAs.append(galg(self.pop.clone()))
self.GAs[i].settings = sub_ga_settings.copy()
self.settings['crossover'] = str(self.crossover).split()[0][1:]
self.settings['selector'] = str(self.pop.selector).split()[0][1:]
self.settings['scaler'] = str(self.pop.scaler).split()[0][1:]
self.settings['genome_type'] = str(
self.pop.model_genome).split()[0][1:]
self._print(self.settings)
if mode[0] == 'p' or mode[0] == 'P':
# XXX: what?
"""
sys.setcheckinterval(1000)
finished = sync.event()
bar = sync.barrier(len(self.GAs))
for ga in self.GAs:
thread.start_new_thread(GA_initializer,(bar,finished,ga))
finished.wait()
sys.setcheckinterval(10)
"""
else:
for ga in self.GAs:
ga.initialize(reseed=False)
cnt = 0
for ga in self.GAs:
self.pop[cnt] = ga.pop.best()
cnt = cnt + 1
self.pop.sort()
self.init_stats()
self.step_time = timer() - b
self.init_dbase()
def initialize(self, mode='serial', reseed=True):
b = timer()
#same as galg
self.test_settings(self.settings)
self.gen = 0
sd = self.settings['rand_seed']
if reseed:
prng.seed(sd)
self.settings['seed_used'] = sd
self._print('initializing... seed = %d' % self.settings['seed_used'])
self.crossover = self.pop[0].crossover # get the crossover op from the first genome
self.pop.settings = self.settings
#end same as galg
#set up my population to hold the best from each sub-pop
self.pop._size(0) #erase any current member of the pop
self.pop._size(self.settings['num_pops'])
self.crossover = self.pop[0].crossover
#extract the galg settings so we don't get a ton of warnings
#and create the sub ga_s
sub_ga_settings = {}
self.GAs = []
for key in galg.valid_settings:
sub_ga_settings[key] = self.settings[key]
for i in range(self.settings['num_pops']):
self.GAs.append(galg(self.pop.clone()))
self.GAs[i].settings = sub_ga_settings.copy()
self.settings['crossover'] = str(self.crossover).split()[0][1:]
self.settings['selector'] = str(self.pop.selector).split()[0][1:]
self.settings['scaler'] = str(self.pop.scaler).split()[0][1:]
self.settings['genome_type'] = str(self.pop.model_genome).split()[0][1:]
self._print(self.settings)
if mode[0] == 'p' or mode[0] == 'P':
# XXX: what?
"""
sys.setcheckinterval(1000)
finished = sync.event()
bar = sync.barrier(len(self.GAs))
for ga in self.GAs:
thread.start_new_thread(GA_initializer,(bar,finished,ga))
finished.wait()
sys.setcheckinterval(10)
"""
else:
for ga in self.GAs: ga.initialize(reseed = False)
cnt = 0
for ga in self.GAs:
self.pop[cnt] = ga.pop.best()
cnt = cnt + 1
self.pop.sort()
self.init_stats()
self.step_time = timer() - b
self.init_dbase()
def initialize(self, reseed=True):
b = timer()
self.test_settings(self.settings)
self.gen = 0
sd = self.settings['rand_seed']
if reseed:
prng.seed(sd)
self.settings['seed_used'] = sd
self._print('initializing... seed = %d' % self.settings['seed_used'])
self.crossover = self.pop.model_genome.crossover # get the crossover op from the first genome
self.pop.settings = self.settings #should these be shared?
self.size_pop(self.settings['pop_size'])
self.settings['crossover'] = str(self.crossover).split()[0][1:]
self.settings['selector'] = str(self.pop.selector).split()[0][1:]
self.settings['scaler'] = str(self.pop.scaler).split()[0][1:]
self.settings['genome_type'] = str(self.pop.model_genome).split()[0][1:]
self.pop.initialize(self.settings);
self.stats = {'selections':0,'crossovers':0,'mutations':0,
'replacements':0,'pop_evals':1,'ind_evals':0}
self.stats.update(self.pop.stats)
self.step_time = timer() - b
self.init_dbase()
