GaussianConstraint¶
-
class
zfit.constraint.
GaussianConstraint
(params, observation, uncertainty)[source]¶ Bases:
zfit.core.constraint.TFProbabilityConstraint
Gaussian constraints on a list of parameters to some observed values with uncertainties.
A Gaussian constraint is defined as the likelihood of params given the observations and uncertainty from a different measurement.
\[\text{constraint} = \text{Gauss}(\text{observation}; \text{params}, \text{uncertainty})\]- Parameters
params (~ParamTypeInput) – The parameters to constraint; corresponds to x in the Gaussian distribution.
observation (
Union
[int
,float
,complex
,Tensor
,ForwardRef
]) – observed values of the parameter; corresponds to mu in the Gaussian distribution.uncertainty (
Union
[int
,float
,complex
,Tensor
,ForwardRef
]) – Uncertainties or covariance/error matrix of the observed values. Can either be a single value, a list of values, an array or a tensor. Corresponds to the sigma of the Gaussian distribution.
- Raises
ShapeIncompatibleError – If params, mu and sigma have incompatible shapes.
-
property
covariance
¶ Return the covariance matrix of the observed values of the parameters constrained.
-
add_cache_deps
(cache_deps, allow_non_cachable=True)¶ Add dependencies that render the cache invalid if they change.
- Parameters
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.
- Raises
TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
-
property
dtype
¶ The dtype of the object.
- Return type
DType
-
get_cache_deps
(only_floating=True)¶ Return a set of all independent
Parameter
that this object depends on.- Parameters
only_floating (
bool
) – If True, only return floatingParameter
- Return type
OrderedSet
-
get_dependencies
(only_floating=True)¶ 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).
- Return type
OrderedSet
-
get_params
(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)¶ Recursively collect parameters that this object depends on according to the filter criteria.
- Which parameters should be included can be steered using the arguments as a filter.
- None: do not filter on this. E.g. floating=None will return parameters that are floating as well as
parameters that are fixed.
True: only return parameters that fulfil this criterion
- False: only return parameters that do not fulfil this criterion. E.g. floating=False will return
only parameters that are not floating.
- Parameters
floating (
Optional
[bool
]) – if a parameter is floating, e.g. iffloating()
returns Trueis_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.
- Return type
Set
[ZfitParameter
]
-
property
name
¶ The name of the object.
- Return type
str
-
property
observation
¶ Return the observed values of the parameters constrained.
-
register_cacher
(cacher)¶ Register a cacher that caches values produces by this instance; a dependent.
- Parameters
cacher (
Union
[ForwardRef
,Iterable
[ForwardRef
]]) –
-
reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
-
sample
(n)¶ Sample n points from the probability density function for the observed value of the parameters.
- Parameters
n – The number of samples to be generated.
Returns: