def __init__( self, conf, **kwargs ):
OptimizationSolver.__init__( self, conf, **kwargs )
conf = self.conf
if conf.is_any_log:
self.log = Log([[r'$||\Psi||$'], [r'$||\nabla \Psi||$'],
[r'$\alpha$'], ['iteration']],
xlabels=['', '', 'all iterations', 'all iterations'],
yscales=conf.yscales,
is_plot=conf.log.plot is not None,
log_filename=conf.log.text,
formats=[['%.8e'], ['%.3e'], ['%.3e'], ['%d']])
else:
self.log = None
def __init__(self, conf, **kwargs):
OptimizationSolver.__init__(self, conf, **kwargs)
conf = self.conf
if conf.is_any_log:
self.log = Log(
[[r'$||\Psi||$'], [r'$||\nabla \Psi||$'], [r'$\alpha$'],
['iteration']],
xlabels=['', '', 'all iterations', 'all iterations'],
yscales=conf.yscales,
is_plot=conf.log.plot is not None,
log_filename=conf.log.text,
formats=[['%.8e'], ['%.3e'], ['%.3e'], ['%d']])
else:
self.log = None
def process_conf(conf, kwargs):
"""
Missing items are set to default values.
Example configuration, all items::
solver_0 = {
'name' : 'fmin_sd',
'kind' : 'opt.fmin_sd',
'i_max' : 10,
'eps_rd' : 1e-5, # Relative delta of objective function
'eps_of' : 1e-4,
'eps_ofg' : 1e-8,
'norm' : nm.Inf,
'ls' : True, # Linesearch.
'ls_method' : 'backtracking', # 'backtracking' or 'full'
'ls0' : 0.25,
'ls_red' : 0.5,
'ls_red_warp' : 0.1,
'ls_on' : 0.99999,
'ls_min' : 1e-5,
'check' : 0,
'delta' : 1e-6,
'output' : None, # 'itc'
'log' : {'text' : 'output/log.txt',
'plot' : 'output/log.png'},
'yscales' : ['linear', 'log', 'log', 'linear'],
}
"""
get = make_get_conf(conf, kwargs)
common = OptimizationSolver.process_conf(conf)
log = get_logging_conf(conf)
log = Struct(name='log_conf', **log)
is_any_log = (log.text is not None) or (log.plot is not None)
return Struct(i_max=get('i_max', 10),
eps_rd=get('eps_rd', 1e-5),
eps_of=get('eps_of', 1e-4),
eps_ofg=get('eps_ofg', 1e-8),
norm=get('norm', nm.Inf),
ls=get('ls', True),
ls_method=get('ls_method', 'backtracking'),
ls0=get('ls0', 0.25),
ls_red=get('ls_red', 0.5),
ls_red_warp=get('ls_red_warp', 0.1),
ls_on=get('ls_on', 0.99999),
ls_min=get('ls_min', 1e-5),
check=get('check', 0),
delta=get('delta', 1e-6),
output=get('output', None),
yscales=get('yscales',
['linear', 'log', 'log', 'linear']),
log=log,
is_any_log=is_any_log) + common
def process_conf(conf, kwargs):
"""
Missing items are set to default values.
Example configuration, all items::
solver_0 = {
'name' : 'fmin_sd',
'kind' : 'opt.fmin_sd',
'i_max' : 10,
'eps_rd' : 1e-5, # Relative delta of objective function
'eps_of' : 1e-4,
'eps_ofg' : 1e-8,
'norm' : nm.Inf,
'ls' : True, # Linesearch.
'ls_method' : 'backtracking', # 'backtracking' or 'full'
'ls0' : 0.25,
'ls_red' : 0.5,
'ls_red_warp' : 0.1,
'ls_on' : 0.99999,
'ls_min' : 1e-5,
'check' : 0,
'delta' : 1e-6,
'output' : None, # 'itc'
'log' : {'text' : 'output/log.txt',
'plot' : 'output/log.png'},
'yscales' : ['linear', 'log', 'log', 'linear'],
}
"""
get = make_get_conf(conf, kwargs)
common = OptimizationSolver.process_conf(conf)
log = get_logging_conf(conf)
log = Struct(name='log_conf', **log)
is_any_log = (log.text is not None) or (log.plot is not None)
return Struct(i_max=get('i_max', 10),
eps_rd=get('eps_rd', 1e-5),
eps_of=get('eps_of', 1e-4),
eps_ofg=get('eps_ofg', 1e-8),
norm=get('norm', nm.Inf),
ls=get('ls', True),
ls_method=get('ls_method', 'backtracking'),
ls0=get('ls0', 0.25),
ls_red=get('ls_red', 0.5),
ls_red_warp=get('ls_red_warp', 0.1),
ls_on=get('ls_on', 0.99999),
ls_min=get('ls_min', 1e-5),
check=get('check', 0),
delta=get('delta', 1e-6),
output=get('output', None),
yscales=get('yscales',
['linear', 'log', 'log', 'linear']),
log=log,
is_any_log=is_any_log) + common
def process_conf(conf):
"""
Missing items are set to default values.
Example configuration, all items:
solver_0 = {
'name' : 'fmin_sd',
'kind' : 'opt.fmin_sd',
'i_max' : 10,
'eps_rd' : 1e-5, # Relative delta of objective function
'eps_of' : 1e-4,
'eps_ofg' : 1e-8,
'norm' : nm.Inf,
'ls' : True, # Linesearch.
'ls_method' : 'backtracking', # 'backtracking' or 'full'
'ls0' : 0.25,
'ls_red' : 0.5,
'ls_red_warp' : 0.1,
'ls_on' : 0.99999,
'ls_min' : 1e-5,
'check' : 0,
'delta' : 1e-6,
'output' : None, # 'itc'
'log' : True,
'yscales' : ['linear', 'log', 'log'],
}
"""
get = conf.get_default_attr
i_max = get("i_max", 10)
eps_rd = get("eps_rd", 1e-5)
eps_of = get("eps_of", 1e-4)
eps_ofg = get("eps_ofg", 1e-8)
norm = get("norm", nm.Inf)
ls = get("ls", True)
ls_method = get("ls_method", "backtracking")
ls0 = get("ls0", 0.25)
ls_red = get("ls_red", 0.5)
ls_red_warp = get("ls_red_warp", 0.1)
ls_on = get("ls_on", 0.99999)
ls_min = get("ls_min", 1e-5)
check = get("check", 0)
delta = get("delta", 1e-6)
output = get("output", None)
log = get("log", True)
yscales = get("yscales", ["linear", "log", "log"])
common = OptimizationSolver.process_conf(conf)
return Struct(**locals()) + common
def process_conf(conf, kwargs):
"""
Missing items are left to SciPy defaults. Unused options are ignored.
Besides 'i_max', use option names according to scipy.optimize
function arguments. The 'i_max' translates either to 'maxiter'
or 'maxfun' as available.
Example configuration::
solver_1 = {
'name' : 'fmin',
'kind' : 'nls.scipy_fmin_like',
'method' : 'bfgs',
'i_max' : 10,
'verbose' : True,
'gtol' : 1e-7
}
"""
get = make_get_conf(conf, kwargs)
common = OptimizationSolver.process_conf(conf)
opts = Struct(method=get('method', 'fmin'),
i_max=get('i_max', 10),
verbose=get('verbose', False)) + common
other = {}
keys = opts.to_dict().keys()
for key, val in conf.to_dict().iteritems():
if key not in keys:
other[key] = val
return opts + Struct(**other)
def process_conf(conf, kwargs):
"""
Missing items are left to SciPy defaults. Unused options are ignored.
Besides 'i_max', use option names according to scipy.optimize
function arguments. The 'i_max' translates either to 'maxiter'
or 'maxfun' as available.
Example configuration::
solver_1 = {
'name' : 'fmin',
'kind' : 'nls.scipy_fmin_like',
'method' : 'bfgs',
'i_max' : 10,
'verbose' : True,
'gtol' : 1e-7
}
"""
get = make_get_conf(conf, kwargs)
common = OptimizationSolver.process_conf(conf)
opts = Struct(method=get('method', 'fmin'),
i_max=get('i_max', 10),
verbose=get('verbose', False)) + common
other = {}
keys = opts.to_dict().keys()
for key, val in conf.to_dict().iteritems():
if key not in keys:
other[key] = val
return opts + Struct(**other)
def __init__(self, conf, **kwargs):
OptimizationSolver.__init__(self, conf, **kwargs)
self.set_method(self.conf)
def __init__(self, conf, **kwargs):
OptimizationSolver.__init__(self, conf, **kwargs)
self.set_method(self.conf)
def __init__(self, conf, **kwargs):
OptimizationSolver.__init__(self, conf, **kwargs)
