baseminimizer¶
Definition of minimizers, wrappers etc.
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class
zfit.minimizers.baseminimizer.
BaseMinimizer
(name, tolerance, verbosity, minimizer_options, strategy=None, **kwargs)[source]¶ Bases:
zfit.util.execution.SessionHolderMixin
,zfit.minimizers.interface.ZfitMinimizer
Minimizer for loss functions.
Additional minimizer_options (given as **kwargs) can be accessed and changed via the attribute (dict) minimizer.minimizer_options
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minimize
(loss: zfit.core.interfaces.ZfitLoss, params: Optional[Iterable[zfit.core.interfaces.ZfitParameter]] = None) → zfit.minimizers.fitresult.FitResult[source]¶ Fully minimize the loss with respect to params.
Parameters: - loss (ZfitLoss) – Loss to be minimized.
- params (list(zfit.Parameter) – The parameters with respect to which to minimize the loss. If None, the parameters will be taken from the loss.
Returns: The fit result.
Return type: FitResult
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sess
¶
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set_sess
(sess: tensorflow.python.client.session.Session)¶ Set the session (temporarily) for this instance. If None, the auto-created default is taken.
Parameters: sess (tf.Session) –
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step
(loss, params: Union[Iterable[zfit.core.interfaces.ZfitParameter], None, Iterable[str]] = None)[source]¶ Perform a single step in the minimization (if implemented).
Parameters: () (params) – Returns:
Raises: NotImplementedError
– if the step method is not implemented in the minimizer.
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tolerance
¶
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class
zfit.minimizers.baseminimizer.
DefaultStrategy
[source]¶ Bases:
zfit.minimizers.baseminimizer.BaseStrategy
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minimize_nan
(loss, params, minimizer, loss_value=None, gradient_values=None)¶
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exception
zfit.minimizers.baseminimizer.
FailMinimizeNaN
[source]¶ Bases:
Exception
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args
¶
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with_traceback
()¶ Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
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class
zfit.minimizers.baseminimizer.
ToyStrategyFail
[source]¶ Bases:
zfit.minimizers.baseminimizer.BaseStrategy
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minimize_nan
(loss, params, minimizer, loss_value=None, gradient_values=None)¶
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