def convert_to_parameter(value) -> "Parameter":
"""Convert a *numerical* to a fixed parameter or return if already a parameter.
Args:
value ():
"""
if isinstance(value, tf.Variable):
return value
# convert to Tensor if not yet
if not isinstance(value, tf.Tensor):
if isinstance(value, complex):
value = ztf.to_complex(value)
else:
value = ztf.to_real(value)
if value.dtype.is_complex:
value = ComplexParameter("FIXED_autoparam_" + str(get_auto_number()),
value=value)
else:
# value = Parameter("FIXED_autoparam_" + str(get_auto_number()), value=value, floating=False)
independend_params = tf.get_collection("zfit_independent")
params = get_dependents(tensor=value, candidates=independend_params)
if params:
value = ComposedParameter("composite_autoparam_" +
str(get_auto_number()),
tensor=value)
else:
value = Parameter("FIXED_autoparam_" + str(get_auto_number()),
value=value,
floating=False)
# value.floating = False
return value
def convert_to_parameter(value,
name=None,
prefer_floating=False) -> "ZfitParameter":
"""Convert a *numerical* to a fixed parameter or return if already a parameter.
Args:
value ():
"""
floating = False
is_python = False
if name is not None:
name = str(name)
if isinstance(
value,
ZfitParameter): # TODO(Mayou36): autoconvert variable. TF 2.0?
return value
elif isinstance(value, tf.Variable):
raise TypeError(
"Currently, cannot autoconvert tf.Variable to zfit.Parameter.")
# convert to Tensor if not yet
if not isinstance(value, tf.Tensor):
is_python = True
if isinstance(value, complex):
value = ztf.to_complex(value)
else:
floating = prefer_floating
value = ztf.to_real(value)
if not run._enable_parameter_autoconversion:
return value
if value.dtype.is_complex:
if name is None:
name = "FIXED_complex_autoparam_" + str(get_auto_number())
value = ComplexParameter(name, value=value, floating=False)
else:
# value = Parameter("FIXED_autoparam_" + str(get_auto_number()), value=value, floating=False)
if is_python:
params = {}
else:
independend_params = tf.get_collection("zfit_independent")
params = get_dependents_auto(tensor=value,
candidates=independend_params)
if params:
if name is None:
name = "composite_autoparam_" + str(get_auto_number())
value = ComposedParameter(name, tensor=value)
else:
if name is None:
name = "FIXED_autoparam_" + str(get_auto_number())
value = Parameter(name, value=value, floating=floating)
# value.floating = False
return value
def poly_complex(*args, real_x=False):
"""Complex polynomial with the last arg being x.
Args:
*args (tf.Tensor or equ.): Coefficients of the polynomial
real_x (bool): If True, x is assumed to be real.
Returns:
tf.Tensor:
"""
args = list(args)
x = args.pop()
if real_x is not None:
pow_func = tf.pow
else:
pow_func = ztf.nth_pow
return tf.add_n(
[coef * ztf.to_complex(pow_func(x, p)) for p, coef in enumerate(args)])