def __init__(self,
criterion,
x0=None,
step=default_step,
line_search=default_line_search,
stop_criterion=default_stop_criterion):
self.criterion = criterion
self.n_variables = criterion.n_variables
self.step = step
self.line_search = line_search
self.stop_criterion = stop_criterion
self.first_guess()
self.optimizer = StandardOptimizer(function=self.criterion,
step=self.step,
line_search=self.line_search,
criterion=self.stop_criterion,
x0=self.current_solution)
self.optimizer.recordHistory = self.callback
class Optimizer(object):
def __init__(self,
criterion,
x0=None,
step=default_step,
line_search=default_line_search,
stop_criterion=default_stop_criterion):
self.criterion = criterion
self.n_variables = criterion.n_variables
self.step = step
self.line_search = line_search
self.stop_criterion = stop_criterion
self.first_guess()
self.optimizer = StandardOptimizer(function=self.criterion,
step=self.step,
line_search=self.line_search,
criterion=self.stop_criterion,
x0=self.current_solution)
self.optimizer.recordHistory = self.callback
def callback(self, **kwargs):
state = self.optimizer.state
iter_ = state['iteration']
crit = state['new_value']
step = state['alpha_step']
if state['iteration'] == 0:
print("Iteration \t Step \t \Criterion")
print("\t%i \t %e \t %e" % (iter_, step, crit))
def first_guess(self, x0=None):
"""
Sets current_solution attribute to initial value.
"""
if x0 is None:
self.current_solution = np.zeros(self.n_variables)
else:
self.current_solution = copy(x0)
def iterate(self):
self.optimizer.iterate()
def __call__(self):
self.optimizer.optimize()
class Optimizer(object):
def __init__(self, criterion,
x0=None,
step=default_step,
line_search=default_line_search,
stop_criterion=default_stop_criterion):
self.criterion = criterion
self.n_variables = criterion.n_variables
self.step = step
self.line_search = line_search
self.stop_criterion = stop_criterion
self.first_guess()
self.optimizer = StandardOptimizer(function=self.criterion,
step=self.step,
line_search=self.line_search,
criterion=self.stop_criterion,
x0=self.current_solution)
self.optimizer.recordHistory = self.callback
def callback(self, **kwargs):
state = self.optimizer.state
iter_ = state['iteration']
crit = state['new_value']
step = state['alpha_step']
if state['iteration'] == 0:
print("Iteration \t Step \t \Criterion")
print("\t%i \t %e \t %e" % (iter_, step, crit))
def first_guess(self, x0=None):
"""
Sets current_solution attribute to initial value.
"""
if x0 is None:
self.current_solution = np.zeros(self.n_variables)
else:
self.current_solution = copy(x0)
def iterate(self):
self.optimizer.iterate()
def __call__(self):
self.optimizer.optimize()
def __init__(self, criterion,
x0=None,
step=default_step,
line_search=default_line_search,
stop_criterion=default_stop_criterion):
self.criterion = criterion
self.n_variables = criterion.n_variables
self.step = step
self.line_search = line_search
self.stop_criterion = stop_criterion
self.first_guess()
self.optimizer = StandardOptimizer(function=self.criterion,
step=self.step,
line_search=self.line_search,
criterion=self.stop_criterion,
x0=self.current_solution)
self.optimizer.recordHistory = self.callback
