本文整理汇总了Python中random.Random.setstate方法的典型用法代码示例。如果您正苦于以下问题:Python Random.setstate方法的具体用法?Python Random.setstate怎么用?Python Random.setstate使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类random.Random
的用法示例。
在下文中一共展示了Random.setstate方法的7个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from random import Random [as 别名]
# 或者: from random.Random import setstate [as 别名]
def main(json_tuning_state, display=False):
"""Computes the new tuning state which contains the new population set and prints it in stdout
so that it can be read by java code
:param json_tuning_state: Current tuning state
:param display:
:return: None
"""
tuning_state = json.loads(json_tuning_state)
pseudo_random_number_generator = Random()
args = {}
if TUNING_STATE_ARCHIVE_KEY not in tuning_state:
pseudo_random_number_generator.seed(time.time())
pso = restartable_pso.restartable_pso(pseudo_random_number_generator)
pso.observer = inspyred.ec.observers.default_observer
pso.terminator = inspyred.ec.terminators.evaluation_termination
pso.topology = inspyred.swarm.topologies.ring_topology
population = pso.evolve(generator=initial_population_generator, evaluator=dummy_fitness_evaluator,
pop_size=POPULATION_SIZE,
bounder=bounder,
maximize=False, max_evaluations=POPULATION_SIZE, **args)
tuning_state = generate_tuning_state(pso, pseudo_random_number_generator, population)
print(tuning_state)
else:
archive = json_to_individual_object(tuning_state[TUNING_STATE_ARCHIVE_KEY])
prev_population = json_to_individual_object(tuning_state[TUNING_STATE_PREV_POPULATION_KEY])
initial_population = json_to_individual_object(tuning_state[TUNING_STATE_CURRENT_POPULATION_KEY])
str_rnd_state = tuning_state[TUNING_STATE_RANDOM_STATE_KEY]
json_rnd_state = json.loads(str_rnd_state)
json_rnd_state[1] = tuple(json_rnd_state[1])
rnd_state = tuple(json_rnd_state)
pseudo_random_number_generator.setstate(rnd_state)
pso = restartable_pso.restartable_pso(pseudo_random_number_generator, _archive=archive,
_previous_population=prev_population)
pso.observer = inspyred.ec.observers.default_observer
pso.terminator = inspyred.ec.terminators.evaluation_termination
pso.topology = inspyred.swarm.topologies.ring_topology
population = pso.evolve(seeds=[cs.candidate for cs in initial_population],
initial_fit=[cs.fitness for cs in initial_population],
generator=None, evaluator=dummy_fitness_evaluator, pop_size=POPULATION_SIZE,
bounder=bounder,
maximize=False, max_evaluations=2 * POPULATION_SIZE, **args)
tuning_state = generate_tuning_state(pso, pseudo_random_number_generator, population)
print(tuning_state)
示例2: __init_rand_gens
# 需要导入模块: from random import Random [as 别名]
# 或者: from random.Random import setstate [as 别名]
def __init_rand_gens(self):
gennum = 10
g = Random(time())
result = [g]
for i in range(gennum - 1):
laststate = g.getstate()
g = Random()
g.setstate(laststate)
g.jumpahead(1000000)
result.append(g)
return result
示例3: _create_generators
# 需要导入模块: from random import Random [as 别名]
# 或者: from random.Random import setstate [as 别名]
def _create_generators(num, delta, firstseed=None):
"""Return list of num distinct generators.
Each generator has its own unique segment of delta elements
from Random.random()'s full period.
Seed the first generator with optional arg firstseed (default
is None, to seed from current time).
"""
from random import Random
g = Random(firstseed)
result = [g]
for i in range(num - 1):
laststate = g.getstate()
g = Random()
g.setstate(laststate)
g.jumpahead(delta)
result.append(g)
return result
示例4: asJson
# 需要导入模块: from random import Random [as 别名]
# 或者: from random.Random import setstate [as 别名]
class SelfGeneratedMessage:
"""SelfGeneratedMessage"""
def asJson(self):
#nowStr=self.now.strftime("%Y-%m-%d %H:%M:%S.%f")
nowStr=self.formatTime(self.now)
publisherIdStr = "publisher-{0}".format(self.publisherId)
info = dict()
info["publisher"]=publisherIdStr
info["time"]=nowStr
info["readings"]=self.readings
res = json.dumps(info)
return res
def formatTime(self, t):
s = t.strftime('%Y-%m-%d %H:%M:%S.%f')
tail = s[-7:]
f = round(float(tail), 3)
temp = "%.3f" % f
return "%s%s" % (s[:-7], temp[1:])
def __init__(self):
self.now = datetime.utcnow()
self.publisherId=randint(0, 5)
self.r1 = Random()
self.r2 = Random()
self.r2.setstate(self.r1.getstate())
self.r2.jumpahead(1024)
numReadingsAdditional=randint(0, 5)
totalReadings = 10+numReadingsAdditional
self.readings=[]
for x in xrange(1, totalReadings):
mean = 2
reading = mean + randint(0, 5) + self.r1.randint(0,1)*self.r2.randint(0,1)*100
self.readings.append(reading)
示例5: main
# 需要导入模块: from random import Random [as 别名]
# 或者: from random.Random import setstate [as 别名]
def main(prng=None, display=False):
if prng is None:
prng = Random()
max_gens = 500
if os.path.exists('ec0.pkl'):
with open('ec0.pkl','rb') as pickle_file:
population = pickle.load(pickle_file)
archive = pickle.load(pickle_file)
random_state = pickle.load(pickle_file)
num_generations = pickle.load(pickle_file)
num_evaluations = pickle.load(pickle_file)
prng.setstate(random_state)
stat_file = open('ga_statistics.csv','w')
ind_file = open('ga_individuals.csv','w')
ea = ec.emo.NSGA2(prng)
ea.variator = [inspyred.ec.variators.blend_crossover,
inspyred.ec.variators.gaussian_mutation]
ea.observer = [inspyred.ec.observers.file_observer,
inspyred.ec.observers.archive_observer,
inspyred.ec.observers.stats_observer]
ea.terminator = [inspyred.ec.terminators.generation_termination,
my_terminator]
ea.num_evaluations = num_evaluations
ea.num_generations = num_generations
ea.archive = archive
final_pop = ea.evolve(seeds=[p.candidate for p in population],
generator=generator,
evaluator=evaluator,
pop_size=30,
maximize=True,
bounder=bounder,
max_generations= max_gens-num_generations,
num_elites=2,
statistics_file=stat_file,
individuals_file=ind_file)
stat_file.close()
ind_file.close()
else:
prng.seed(1234)
stat_file = open('ga_statistics.csv', 'w')
ind_file = open('ga_individuals.csv', 'w')
ea = ec.emo.NSGA2(prng)
ea.variator = [inspyred.ec.variators.blend_crossover,
inspyred.ec.variators.gaussian_mutation]
ea.observer = [inspyred.ec.observers.file_observer,
inspyred.ec.observers.archive_observer,
inspyred.ec.observers.stats_observer]
ea.terminator = [inspyred.ec.terminators.generation_termination,
my_terminator]
final_pop = ea.evolve(generator=generator,
evaluator=evaluator,
pop_size=30,
maximize=True,
bounder=bounder,
max_generations=max_gens,
num_elites=2,
statistics_file=stat_file,
individuals_file=ind_file)
stat_file.close()
ind_file.close()
if display:
# Sort and print the best individual, who will be at index 0.
final_pop.sort(reverse=True)
print('Terminated due to %s.' % ea.termination_cause)
print(final_pop[0])
return ea
示例6: gen_result
# 需要导入模块: from random import Random [as 别名]
# 或者: from random.Random import setstate [as 别名]
def gen_result():
r = Random(0)
r.setstate(state)
return self.produce(r)
示例7: __init__
# 需要导入模块: from random import Random [as 别名]
# 或者: from random.Random import setstate [as 别名]
class Cyana:
"""
Front-end for CYANA. Provides methods needed to run CYANA.
Current implementation includes only methods needed to run CYANA in batch
mode using macros. TODO: more features, control of screen output, support
for a session mode CYANA, i.e. a true API to CYANA/INCLAN.
"""
def __init__(self, input, output=None, fixed_angles=None, rg_limits=None,
use_limits=True, seed=None, state=None, jump=None, tally=0,
tally_digits=3, name=None, verbose=False, debug=False):
# store and/or process inputs
self.input = str(input)
self.rg_limits = rg_limits
self.use_limits = bool(use_limits)
self.tally = int(tally)
self.tally_digits = int(tally_digits)
if output != None:
self.output = str(output)
else:
self.output = self.input + '-'
self.fixed_angles = fixed_angles
if fixed_angles != None:
if type(fixed_angles) != list:
if type(fixed_angles) == tuple:
self.fixed_angles = list(fixed_angles)
else:
self.fixed_angles = [fixed_angles]
for i in range(len(self.fixed_angles)):
if type(self.fixed_angles[i]) in [list, tuple]:
lo, hi = str(self.fixed_angles[i][0]), \
str(self.fixed_angles[i][1])
elif re.search('([0-9]+).+([0-9]+)', str(self.fixed_angles[i])):
lo, hi = re.search('([0-9]+).+([0-9]+)', \
str(self.fixed_angles[i])).groups()
else:
lo = hi = str(self.fixed_angles[i])
self.fixed_angles[i] = '%s..%s'%(lo,hi)
if name is None:
self.name = 'temp-%d'%os.getpid()
else:
self.name = str(name)
if debug: loki.setLevel(logging.DEBUG)
elif verbose: loki.setLevel(logging.INFO)
# check that required input files exist
if not os.path.isfile(self.input+'.seq'):
raise ArgumentError, ('No sequence file found.', 'Cyana')
if self.fixed_angles and not os.path.isfile(self.input + '.pdb'):
raise ArgumentError, ('No model PDB file found for angle fixes.',
'Cyana')
# setup temporary files
os.system('cp -f %s.seq %s.seq'%(self.input, self.name))
loki.info('Readied the sequence file for further use.')
if self.use_limits:
for suffix in ['lol', 'upl', 'aco', 'xplor']:
if os.path.isfile(self.input + '.' + suffix):
os.system('cp -f %s.%s %s.%s'%(self.input, suffix,
self.name, suffix))
loki.info("Readied a '%s' file for further use."%suffix)
if self.fixed_angles:
os.system('cp -f %s.pdb %s.pdb'%(self.input, self.name))
# initialise the random generator
self.randgen = Random(seed)
if state == None:
if jump != None:
if sys.version < '2.2':
loki.warn('Python older than 2.2. Problems may arise.')
elif sys.version > '2.2' and sys.version < '2.3':
# this should provide enough room for each run to be in
# unique state space
self.randgen.jumpahead(jump*self.amount*1.1)
else:
self.randgen.jumpahead(jump)
else:
self.randgen.setstate(state)
self.__last_macro_size__ = None
def anneal(self, amount, snippets=None, max_temp=4.0, min_temp=0.9,
steps=5000):
"""
Anneal.
"""
amount = int(amount)
if snippets is None:
snippets = amount
else:
snippets = int(snippets)
max_temp = float(max_temp)
min_temp = float(min_temp)
steps = int(steps)
if snippets > amount:
loki.warn('Snippet size larger than total amount. '
'Running in one go.')
snippets = amount
# Current version of Cyana (2.1?) seems to be broken as tend is not
# supported by anneal. FIXME when corrected.
# self.__calc_end__ = 'anneal thigh=%1.3f tend=%1.3f steps=%d'%( \
# max_temp, min_temp, steps)
self.__calc_end__ = 'anneal thigh=%1.3f steps=%d'%( \
max_temp, steps)
#.........这里部分代码省略.........