def __init__(self,
start=[],
directions=[],
start_step_size=1.0,
stop_step_size=1e-10,
scaling_factor=0.5,
up_scaling_factor=1,
max_iter=inf,
max_bound=[],
min_bound=[],
red_pars=[],
parallelization={},
**kwargs):
ParameterOptimizationBase.__init__(self, **kwargs)
# extract parallelization dict for subflow handler
SubflowHandler.__init__(self, parallelization)
dim = len(self.variables)
if start != []:
x_opt = self.get_vector(start)
else:
x_opt = ones(dim)
self._log("No starting vector given. Vector of all ones taken.",
level=logging.CRITICAL)
if directions == []:
directions = list(vstack((identity(dim), -identity(dim))))
self._log("No search directions given! Using unit directions.",
level=logging.WARNING)
directions = [tuple(d) for d in directions]
# delete duplicates
directions = list(set(directions))
max_bound = self.get_vector(
max_bound) if max_bound != [] else inf * ones(dim)
min_bound = self.get_vector(
min_bound) if min_bound != [] else -inf * ones(dim)
assert((x_opt < max_bound).all() and (x_opt > min_bound).all()), \
"Starting point is not valid!"
# copy all params which will be changed during processing
init_params = {
"x_opt": x_opt,
"directions": directions,
"step_size": start_step_size
}
self.set_permanent_attributes(x_opt=x_opt,
directions=directions,
step_size=start_step_size,
stop_step_size=stop_step_size,
scaling_factor=scaling_factor,
up_scaling_factor=up_scaling_factor,
max_iter=max_iter,
min_bound=min_bound,
max_bound=max_bound,
init_params=init_params)
def __init__(self, start=[], directions=[], start_step_size=1.0,
stop_step_size=1e-10, scaling_factor=0.5, up_scaling_factor=1,
max_iter=inf, max_bound=[], min_bound=[], red_pars=[],
parallelization={},
**kwargs):
ParameterOptimizationBase.__init__(self, **kwargs)
# extract parallelization dict for subflow handler
SubflowHandler.__init__(self, parallelization)
dim = len(self.variables)
if start != []:
x_opt = self.get_vector(start)
else:
x_opt = ones(dim)
self._log("No starting vector given. Vector of all ones taken.",
level = logging.CRITICAL)
if directions == []:
directions = list(vstack((identity(dim),-identity(dim))))
self._log("No search directions given! Using unit directions.",
level = logging.WARNING)
directions = [tuple(d) for d in directions]
# delete duplicates
directions = list(set(directions))
max_bound = self.get_vector(max_bound) if max_bound != [] else inf*ones(dim)
min_bound = self.get_vector(min_bound) if min_bound != [] else -inf*ones(dim)
assert((x_opt < max_bound).all() and (x_opt > min_bound).all()), \
"Starting point is not valid!"
# copy all params which will be changed during processing
init_params = {"x_opt": x_opt, "directions": directions,
"step_size": start_step_size}
self.set_permanent_attributes(x_opt = x_opt,
directions = directions,
step_size = start_step_size,
stop_step_size = stop_step_size,
scaling_factor = scaling_factor,
up_scaling_factor = up_scaling_factor,
max_iter = max_iter,
min_bound = min_bound,
max_bound = max_bound,
init_params = init_params)
def __init__(self, ranges, parallelization={}, **kwargs):
ParameterOptimizationBase.__init__(self, **kwargs)
# extract parallelization dict for subflow handler
SubflowHandler.__init__(self, **parallelization)
self.set_permanent_attributes(grid=self.search_grid(ranges))
def __init__(self, ranges, *args, **kwargs):
ParameterOptimizationBase.__init__(self, *args, **kwargs)
# extract parallelization dict for subflow handler
SubflowHandler.__init__(self, **kwargs.get('parallelization', {}))
self.set_permanent_attributes(grid=self.search_grid(ranges))
def __init__(self, ranges=None, grid=None, *args, **kwargs):
ParameterOptimizationBase.__init__(self, *args, **kwargs)
# extract parallelization dict for subflow handler
SubflowHandler.__init__(self, **kwargs.get('parallelization',{}))
self.set_permanent_attributes(
grid=self.search_grid(ranges) if ranges is not None else grid)