def _read_configs(cls, config_object, list_output = True):
""" Allow for different type of configs to be passed (<dic>, <list<dic>>,
<str>, <list<str>>)
"""
if(ut.is_dico(config_object)):
configs = [config_object] if(list_output) else config_object
elif(ut.is_list(config_object)):
configs = [cls._read_configs(conf, list_output = False) for conf in config_object]
elif(ut.is_str(config_object)):
configs = ut.file_to_dico(config_object)
if(list_output):
configs = [configs]
else:
raise NotImplementedError()
return configs
def gen_rdmnb_from_string(self, method_rdm, dim=1):
"""
#TODO: change name gen_rdm_XXX // use genrdmdunction //
#Old name = GenRdmNumbersFromStr
Purpose:
Generate Random Sequences based on a string or list of string
{X0, XN-1} is represented as a N row
NxD matrix if each RandomVar has dim D
More generally dim = [dim_pop, dim_RV]
"""
functmp = self.gen_rdmfunc_from_string(method_rdm, dim)
if (ut.is_list(functmp)):
res = [f() for f in functmp]
else:
res = functmp()
return res
def gen_rdmfunc_from_string(self, method_rdm, dim=1):
"""return a function that when called return random variables according
to the distribution described by the string, with specific dim
convention: dim[-1] correspond to the dim of the RV while dim[:-1] to
the size of the population
TODO: works only for 1D dim
"""
if ut.is_list(method_rdm):
# return a lst of function
# should it be a function returning a list??
func = [
self.gen_rdmfunc_from_string(meth, dim) for meth in method_rdm
]
else:
args = ut.splitString(method_rdm)
if (ut.is_int(dim)):
dimRV = dim
else:
dimRV = dim[-1]
if (args[0] in ['uniform', 'normal']):
if (len(args) == 1):
first_args = np.repeat(0, dimRV)
second_args = np.repeat(1, dimRV)
elif (len(args) == 3):
first_args = np.repeat(float(args[1]), dimRV)
second_args = np.repeat(float(args[2]), dimRV)
elif (len(args) == (1 + 2 * dimRV)):
first_args = np.array(
[float(args[1 + 2 * d]) for d in range(dimRV)])
second_args = np.array(
[float(args[2 + 2 * d]) for d in range(dimRV)])
else:
raise NotImplementedError()
if (dim == 1):
# such that function return a value instead of an array
# may change
first_args, second_args = first_args[0], second_args[0]
dim = None
if args[0] == 'normal':
def func():
return self.normal(first_args, second_args, size=dim)
else:
def func():
return self.uniform(first_args, second_args, size=dim)
elif (args[0] == 'determ'):
if (len(args) == 1):
constant = np.repeat(0, dim)
elif (len(args) == 2):
constant = np.repeat(float(args[1]), dim)
elif (len(args) == (1 + dimRV)):
constant = np.array(args[1:])
if (ut.is_list(dim)):
dim_pop = np.product(dim[:-1])
constant = np.tile(constant, dim_pop).reshape(dim)
else:
raise NotImplementedError()
def func():
return constant
else:
raise NotImplementedError()
return func