zfit.loss.
BaseLoss
Bases: zfit.core.interfaces.ZfitLoss, zfit.core.baseobject.BaseNumeric
zfit.core.interfaces.ZfitLoss
zfit.core.baseobject.BaseNumeric
A “simultaneous fit” can be performed by giving one or more model, data, fit_range to the loss. The length of each has to match the length of the others.
model (Union[ForwardRef, Iterable[ForwardRef]]) – The model or models to evaluate the data on
Union
ForwardRef
Iterable
data (Union[ForwardRef, Iterable[ForwardRef]]) – Data to use
fit_range (Union[ForwardRef, Tensor, ndarray, Iterable[float], float, Tuple[float], List[float], bool, None]) – The fitting range. It’s the norm_range for the models (if
Tensor
ndarray
float
Tuple
List
bool
None
they – have a norm_range) and the data_range for the data.
constraints (Union[Iterable[Union[ForwardRef, Callable]], ForwardRef, Callable, None]) – A Tensor representing a loss constraint. Using zfit.constraint.* allows for easy use of predefined constraints.
Callable
add_cache_deps
Add dependencies that render the cache invalid if they change.
cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –
allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.
TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
dtype
The dtype of the object
DType
get_cache_deps
Return a set of all independent Parameter that this object depends on.
Parameter
only_floating (bool) – If True, only return floating Parameter
OrderedSet
get_dependencies
DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).
get_params
Recursively collect parameters that this object depends on according to the filter criteria.
parameters that are fixed.
True: only return parameters that fulfil this criterion
only parameters that are not floating.
floating (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True
Optional
floating()
is_yield (Optional[bool]) – if a parameter is a yield of the _current_ model. This won’t be applied recursively, but may include yields if they do also represent a parameter parametrizing the shape. So if the yield of the current model depends on other yields (or also non-yields), this will be included. If, however, just submodels depend on a yield (as their yield) and it is not correlated to the output of our model, they won’t be included.
extract_independent (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.
Set[ZfitParameter]
Set
ZfitParameter
register_cacher
Register a cacher that caches values produces by this instance; a dependent.
cacher (Union[ForwardRef, Iterable[ForwardRef]]) –
reset_cache_self
Clear the cache of self and all dependent cachers.