constraint¶
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zfit.constraint.
nll_gaussian
(params: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], observation: Union[int, float, complex, tensorflow.python.framework.ops.Tensor], uncertainty: Union[int, float, complex, tensorflow.python.framework.ops.Tensor]) → tensorflow.python.framework.ops.Tensor[source]¶ Return negative log likelihood graph for gaussian constraints on a list of parameters. :param params: The parameters to constraint :type params: list(zfit.Parameter) :param observation: observed values of the parameter :type observation: numerical, list(numerical) :param uncertainty: Uncertainties or covariance/error
matrix of the observed values. Can either be a single value, a list of values, an array or a tensorReturns: the constraint object Return type: GaussianConstraint Raises: ShapeIncompatibleError
– if params, mu and sigma don’t have the same size
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class
zfit.constraint.
SimpleConstraint
(func: Callable, params: Optional[Dict[str, zfit.core.interfaces.ZfitParameter]])[source]¶ Bases:
zfit.core.constraint.BaseConstraint
Constraint from a (function returning a) Tensor.
The parameters are named “param_{i}” with i starting from 0 and corresponding to the index of params.
Parameters: - func – Callable that constructs the constraint and returns a tensor.
- params – The dependents (independent zfit.Parameter) of the loss. If not given, the dependents are figured out automatically.
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add_cache_dependents
(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)¶ Add dependents that render the cache invalid if they change.
Parameters: - cache_dependents (ZfitCachable) –
- 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.
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copy
(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject¶
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dtype
¶ The dtype of the object
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get_dependents
(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])¶ Return a set of all independent
Parameter
that this object depends on.Parameters: only_floating (bool) – If True, only return floating Parameter
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get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[ZfitParameter]¶ Return the parameters. If it is empty, automatically return all floating variables.
Parameters: - () (names) – If True, return only the floating parameters.
- () – The names of the parameters to return.
Returns: Return type: list(ZfitParameters)
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graph_caching_methods
= []¶
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name
¶ The name of the object.
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old_graph_caching_methods
= []¶
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params
¶
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register_cacher
(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])¶ Register a cacher that caches values produces by this instance; a dependent.
Parameters: () (cacher) –
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reset_cache
(reseter: zfit.util.cache.ZfitCachable)¶
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reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
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value
()¶
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class
zfit.constraint.
GaussianConstraint
(params: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], observation: Union[int, float, complex, tensorflow.python.framework.ops.Tensor], uncertainty: Union[int, float, complex, tensorflow.python.framework.ops.Tensor])[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.
\[constraint = Gauss(observation; params, uncertainty)\]Parameters: - params (list(zfit.Parameter)) – The parameters to constraint; corresponds to mu in the Gaussian distribution.
- observation (numerical, list(numerical)) – observed values of the parameter; corresponds to the x argument in the Gaussian distribution.
- uncertainty (numerical, list(numerical) or array/tensor) – 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 don’t have incompatible shapes-
add_cache_dependents
(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)¶ Add dependents that render the cache invalid if they change.
Parameters: - cache_dependents (ZfitCachable) –
- 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.
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copy
(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject¶
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covariance
¶ Return the covariance matrix of the observed values of the parameters constrained.
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distribution
¶
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dtype
¶ The dtype of the object
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get_dependents
(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])¶ Return a set of all independent
Parameter
that this object depends on.Parameters: only_floating (bool) – If True, only return floating Parameter
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get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[ZfitParameter]¶ Return the parameters. If it is empty, automatically return all floating variables.
Parameters: - () (names) – If True, return only the floating parameters.
- () – The names of the parameters to return.
Returns: Return type: list(ZfitParameters)
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graph_caching_methods
= []¶
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name
¶ The name of the object.
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observation
¶ Return the observed values of the parameters constrained.
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old_graph_caching_methods
= []¶
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params
¶
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register_cacher
(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])¶ Register a cacher that caches values produces by this instance; a dependent.
Parameters: () (cacher) –
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reset_cache
(reseter: zfit.util.cache.ZfitCachable)¶
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reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
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sample
(n)¶ Sample n points from the probability density function for the observed value of the parameters.
Parameters: n (int, tf.Tensor) – The number of samples to be generated. Returns: n_samples) Return type: Dict(Parameter
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value
()¶