def line_search(cls):
optimize.initialize_search()
while True:
cls.evaluate_function()
optimize.update_status()
if optimize.isdone:
optimize.finalize_search()
break
elif optimize.step_count < PAR.STEPMAX:
optimize.compute_step()
continue
else:
retry = optimize.retry_status
if retry:
print ' Line search failed... retry'
optimize.restart()
cls.line_search()
break
else:
print ' Line search failed... abort'
sys.exit(-1)
def line_search(cls):
optimize.initialize_search()
while True:
cls.evaluate_function()
optimize.update_status()
if optimize.isdone:
optimize.finalize_search()
break
elif optimize.step_count < PAR.STEPMAX:
optimize.compute_step()
continue
else:
retry = optimize.retry_status
if retry:
print ' Line search failed... retry'
optimize.restart()
cls.line_search()
break
else:
print ' Line search failed... abort'
sys.exit(-1)
def iterate_search(self):
""" First, calls self.evaluate_function, which carries out a forward
simulation given the current trial model. Then calls
optimize.update_status, which maintains search history and checks
stopping conditions.
"""
if PAR.VERBOSE > 0:
print " trial step", optimize.step_count + 1
self.evaluate_function()
optimize.update_status()
def iterate_search(self):
""" First, calls self.evaluate_function, which carries out a forward
simulation given the current trial model. Then calls
optimize.update_status, which maintains search history and checks
stopping conditions.
"""
if PAR.VERBOSE:
print " trial step", optimize.step_count+1
self.evaluate_function()
optimize.update_status()