def __init__(self, no_vars, mins, maxes, comm, stop_flag, p0=None,
max_iter=5, central=False):
super(LevmarOptimization, self).__init__(no_vars, mins, maxes, comm,
stop_flag)
self.timer = Timer()
# If starting vector is not specified, start with min values.
self.p0 = np.array(p0 or mins)
self.max_iter = max_iter
self.central = central
self.prepare()
def __init__(self, no_vars, mins, maxes, comm, stop_flag, seed=False,
allele=False, size=200, generations=100):
super(GeneticOptimization, self).__init__(no_vars, mins, maxes, comm,
stop_flag)
self.seed = seed
self.allele = allele
self.size = size
self.timer = Timer()
self.generations = generations
self.prepare()
class GeneticOptimization(OptimizationStep):
"Genetic optimization step."
def __init__(self, no_vars, mins, maxes, comm, stop_flag, seed=False,
allele=False, size=200, generations=100):
super(GeneticOptimization, self).__init__(no_vars, mins, maxes, comm,
stop_flag)
self.seed = seed
self.allele = allele
self.size = size
self.timer = Timer()
self.generations = generations
self.prepare()
def __prepare_genome(self):
u"Initialize genome with constraints."
if self.allele: # Built-in allele version.
self.genome = AlleleG1DList(self.no_vars, constr_min=self.mins,
constr_max=self.maxes)
else: # Custom, ported genetic operators.
self.genome = CustomG1DList(self.no_vars)
self.genome.setParams(min_constr=self.mins, max_constr=self.maxes)
self.genome.evaluator.set(GeneticObjective(self.comm))
def __prepare_engine(self):
u"Prepare the GA engine."
# Set GA engine parameters.
self.ga = CustomGSimpleGA(self.genome, self.seed)
self.ga.setPopulationSize(self.size)
self.ga.setGenerations(self.generations)
self.ga.setParams(stop_file=self.stop_flag)
self.ga.setParams(timer=self.timer)
def prepare(self):
self.__prepare_genome()
self.__prepare_engine()
def run(self):
u"Fire the Genetic Algorithm Engine."
printf("Starting GA: %d generations of %d individuals" % (
self.generations, self.size))
self.timer.start()
self.ga.evolve()
self.__post_run()
# Return the best parameters found.
return list(self.ga.bestIndividual().getInternalList())
def __post_run(self):
stats_step_callback(self.ga) # Display final statistics.
# Evolution is stoped.
run_time = self.timer.stop()
print "GA finished in %g s." % run_time
class LevmarOptimization(OptimizationStep):
"Levenberg-Marquardt optimization step."
def __init__(self, no_vars, mins, maxes, comm, stop_flag, p0=None,
max_iter=5, central=False):
super(LevmarOptimization, self).__init__(no_vars, mins, maxes, comm,
stop_flag)
self.timer = Timer()
# If starting vector is not specified, start with min values.
self.p0 = np.array(p0 or mins)
self.max_iter = max_iter
self.central = central
self.prepare()
def prepare(self):
# Starting residuals (or observed values).
self.y = np.array([0] * self.no_vars)
self.bounds = zip(self.mins, self.maxes)
def run(self):
printf("Starting LM: %d iterations" % self.max_iter)
objective = LevmarObjective(self.comm)
self.timer.start()
output = levmar(objective, p0=self.p0, y=self.y, bounds=self.bounds,
maxit=self.max_iter, cdif=self.central, eps1=1e-15, eps2=1e-15,
eps3=1e-20, full_output=True, breakf=self.__breakf)
self.__post_run(output)
return list(output.p)
def __post_run(self, lm_output):
run_time = self.timer.stop()
info = lm_output.info
printf("Iteration %d of %d, ||error|| %g" % (info[2], self.max_iter,
info[1][0]))
print "LM finished in %g s, with reason: %s" % (run_time, info[3])
def __breakf(self, i, maxit, p, error):
pstr = "Iteration %d of %d, ||error|| %g" % (i, maxit, error)
self.timer.lap()
if i > 0:
iter_left = maxit - i
mean_time = self.timer.time_so_far / i
pstr += "\t(est. time to end=%d s.)" % (iter_left * mean_time)
printf(pstr)
if check_stop_flag(self.stop_flag):
return 1
