egttools.numerical.results.AGoSResult¶

class AGoSResult(mean_gradient, se_gradient, nb_runs, strategy_names=None)[source]¶

Average gradient of selection G^A(x) from a network MC estimator.

Methods

Attributes

Return repr(self).

__annotations__ = {'mean_gradient': 'np.ndarray', 'nb_runs': 'int', 'se_gradient': 'np.ndarray', 'strategy_names': 'Optional[Sequence[str]]'}¶

__dataclass_fields__ = {'mean_gradient': Field(name='mean_gradient',type='np.ndarray',default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'nb_runs': Field(name='nb_runs',type='int',default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'se_gradient': Field(name='se_gradient',type='np.ndarray',default=<dataclasses._MISSING_TYPE object>,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'strategy_names': Field(name='strategy_names',type='Optional[Sequence[str]]',default=None,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=False,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD)}¶

__dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,order=False,unsafe_hash=False,frozen=False)¶

__match_args__ = ('mean_gradient', 'se_gradient', 'nb_runs', 'strategy_names')¶

property roots: list¶

, 0] (first strategy’s gradient).