def default_opt_arg(self):
"""Provide default optional arguments by the user.
Should be given as a dictionary when overridden.
"""
return {
opt: default_arg_from_bounds(bnd)
for (opt, bnd) in self.default_opt_arg_bounds().items()
}
def _init_guess(bounds, current, default, typ, para_name):
"""Proper determination of initial guess."""
if typ == "default":
if bounds[0] < default < bounds[1]:
return default
return default_arg_from_bounds(bounds)
if typ == "current":
return current
raise ValueError("CovModel.fit: unknown init_guess: '{}'".format(typ))
def _pre_init_guess(model, init_guess, mean_x=1.0, mean_y=1.0):
# init guess should be a dict
if not isinstance(init_guess, dict):
init_guess = {"default": init_guess}
# "default" init guess is the respective default value
default_guess = init_guess.pop("default", "default")
if default_guess not in ["default", "current"]:
raise ValueError(f"fit_variogram: unknown def. guess: {default_guess}")
default = default_guess == "default"
# check invalid names for given init guesses
invalid_para = set(init_guess) - set(model.iso_arg + ["anis"])
if invalid_para:
raise ValueError(f"fit_variogram: unknown init guess: {invalid_para}")
bnd = model.arg_bounds
# default length scale is mean of given bin centers (respecting "rescale")
init_guess.setdefault(
"len_scale", mean_x * model.rescale if default else model.len_scale
)
# init guess for variance and nugget is mean of given variogram
for par in ["var", "nugget"]:
init_guess.setdefault(par, mean_y if default else getattr(model, par))
# anis setting
init_guess.setdefault(
"anis", default_arg_from_bounds(bnd["anis"]) if default else model.anis
)
# correctly handle given values for anis (need a list of values)
init_guess["anis"] = list(set_anis(model.dim, init_guess["anis"]))
# set optional arguments
for opt in model.opt_arg:
init_guess.setdefault(
opt,
default_arg_from_bounds(bnd[opt])
if default
else getattr(model, opt),
)
# convert all init guesses to float (except "anis")
for arg in model.iso_arg:
init_guess[arg] = float(init_guess[arg])
return init_guess
def _init_curve_fit_para(model, para, init_guess, constrain_sill, sill, anis):
"""Create initial guess and bounds for fitting."""
low_bounds = []
top_bounds = []
init_guess_list = []
for par in DEFAULT_PARA:
if para[par]:
low_bounds.append(model.arg_bounds[par][0])
if par == "var" and constrain_sill: # var <= sill in this case
top_bounds.append(sill)
else:
top_bounds.append(model.arg_bounds[par][1])
init_guess_list.append(
_init_guess(
bounds=[low_bounds[-1], top_bounds[-1]],
current=getattr(model, par),
default=1.0,
typ=init_guess,
para_name=par,
))
for opt in model.opt_arg:
if para[opt]:
low_bounds.append(model.arg_bounds[opt][0])
top_bounds.append(model.arg_bounds[opt][1])
init_guess_list.append(
_init_guess(
bounds=[low_bounds[-1], top_bounds[-1]],
current=getattr(model, opt),
default=model.default_opt_arg()[opt],
typ=init_guess,
para_name=opt,
))
if anis:
low_bounds += [model.anis_bounds[0]] * (model.dim - 1)
top_bounds += [model.anis_bounds[1]] * (model.dim - 1)
if init_guess == "default":
def_arg = default_arg_from_bounds(model.anis_bounds)
init_guess_list += [def_arg] * (model.dim - 1)
elif init_guess == "current":
init_guess_list += list(model.anis)
else:
raise ValueError(
"CovModel.fit: unknown init_guess: '{}'".format(init_guess))
return (low_bounds, top_bounds), init_guess_list
def _init_guess(bounds, default):
"""Proper determination of initial guess."""
if bounds[0] < default < bounds[1]:
return default
return default_arg_from_bounds(bounds)