pcntoolkit.math_functions.prior =============================== .. py:module:: pcntoolkit.math_functions.prior Attributes ---------- .. autoapisummary:: pcntoolkit.math_functions.prior.DEFAULT_PRIOR_ARGS pcntoolkit.math_functions.prior.PM_DISTMAP Classes ------- .. autoapisummary:: pcntoolkit.math_functions.prior.BasePrior pcntoolkit.math_functions.prior.CenteredRandomPrior pcntoolkit.math_functions.prior.LinearPrior pcntoolkit.math_functions.prior.Prior pcntoolkit.math_functions.prior.RandomPrior Functions --------- .. autoapisummary:: pcntoolkit.math_functions.prior.make_prior pcntoolkit.math_functions.prior.prior_from_args Module Contents --------------- .. py:class:: BasePrior(name: str = 'theta', dims: Optional[Union[Tuple[str, Ellipsis], str]] = None, mapping: str = 'identity', mapping_params: tuple = None, **kwargs) Bases: :py:obj:`abc.ABC` Helper class that provides a standard way to create an ABC using inheritance. .. py:method:: __eq__(other: BasePrior) .. py:method:: apply_mapping(x: Any) -> Any .. py:method:: compile(model: pymc.Model, X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray) -> Any .. py:method:: from_dict(dict: BasePrior.from_dict.dict, version: str | None = None) -> BasePrior :classmethod: .. py:method:: set_name(name: str) -> None :abstractmethod: .. py:method:: to_dict() .. py:method:: transfer(idata: arviz.InferenceData, **kwargs) -> BasePrior :abstractmethod: .. py:method:: update_data(model, X, be, be_maps, Y) :abstractmethod: .. py:property:: dims .. py:property:: has_random_effect :type: bool :abstractmethod: .. py:attribute:: mapping :value: 'identity' .. py:attribute:: mapping_params :value: (0, 1) .. py:attribute:: name :value: 'theta' .. py:attribute:: sample_dims :value: () .. py:class:: CenteredRandomPrior(mu: Optional[BasePrior] = None, sigma: Optional[BasePrior] = None, name: str = 'theta', dims: Optional[Union[Tuple[str, Ellipsis], str]] = None, mapping: str = 'identity', mapping_params: tuple[float, Ellipsis] = None, **kwargs) Bases: :py:obj:`BasePrior` Helper class that provides a standard way to create an ABC using inheritance. .. py:method:: from_dict(dict: CenteredRandomPrior.from_dict.dict, version: str | None = None) -> CenteredRandomPrior :classmethod: .. py:method:: set_name(name: str) .. py:method:: to_dict() .. py:method:: transfer(idata: arviz.InferenceData, **kwargs) -> RandomPrior .. py:method:: update_data(model: pymc.Model, X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray) .. py:property:: dims .. py:property:: has_random_effect .. py:attribute:: mu .. py:attribute:: offsets .. py:attribute:: sample_dims :value: ('observations',) .. py:attribute:: scaled_offsets .. py:attribute:: sigma .. py:attribute:: sigmas .. py:class:: LinearPrior(slope: Optional[BasePrior] = None, intercept: Optional[BasePrior] = None, name: str = 'theta', dims: Optional[Union[Tuple[str, Ellipsis], str]] = None, mapping: str = 'identity', mapping_params: tuple[float, Ellipsis] = None, basis_function: pcntoolkit.math_functions.basis_function.BasisFunction = LinearBasisFunction(), **kwargs) Bases: :py:obj:`BasePrior` Helper class that provides a standard way to create an ABC using inheritance. .. py:method:: from_dict(dict: LinearPrior.from_dict.dict, version: str | None = None) -> LinearPrior :classmethod: .. py:method:: set_name(name) .. py:method:: to_dict() .. py:method:: transfer(idata: arviz.InferenceData, **kwargs) -> LinearPrior .. py:method:: update_data(model: pymc.Model, X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray) .. py:attribute:: basis_function .. py:property:: dims .. py:property:: has_random_effect .. py:attribute:: intercept .. py:attribute:: sample_dims :value: ('observations',) .. py:attribute:: slope .. py:class:: Prior(name: str = 'theta', dims: Optional[Union[Tuple[str, Ellipsis], str]] = None, mapping: str = 'identity', mapping_params: tuple[float, Ellipsis] = None, dist_name: str = 'Normal', dist_params: Tuple[float | int | list[float | int], Ellipsis] = None, **kwargs) Bases: :py:obj:`BasePrior` Helper class that provides a standard way to create an ABC using inheritance. .. py:method:: from_dict(dict: Prior.from_dict.dict, version: str | None = None) :classmethod: .. py:method:: set_name(name: str) -> None .. py:method:: to_dict() .. py:method:: transfer(idata: arviz.InferenceData, **kwargs) -> Prior .. py:method:: update_data(model: pymc.Model, X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray) .. py:attribute:: dist_name :value: 'Normal' .. py:attribute:: dist_params :value: (0, 10.0) .. py:property:: has_random_effect .. py:attribute:: sample_dims :value: () .. py:class:: RandomPrior(mu: Optional[BasePrior] = None, sigma: Optional[BasePrior] = None, name: str = 'theta', dims: Optional[Union[Tuple[str, Ellipsis], str]] = None, mapping: str = 'identity', mapping_params: tuple[float, Ellipsis] = None, **kwargs) Bases: :py:obj:`BasePrior` Helper class that provides a standard way to create an ABC using inheritance. .. py:method:: from_dict(dict: RandomPrior.from_dict.dict, version: str | None = None) -> RandomPrior :classmethod: .. py:method:: set_name(name: str) .. py:method:: to_dict() .. py:method:: transfer(idata: arviz.InferenceData, **kwargs) -> RandomPrior .. py:method:: update_data(model: pymc.Model, X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray) .. py:property:: dims .. py:property:: has_random_effect .. py:attribute:: mu .. py:attribute:: offsets .. py:attribute:: sample_dims :value: ('observations',) .. py:attribute:: scaled_offsets .. py:attribute:: sigma .. py:attribute:: sigmas .. py:function:: make_prior(name: str = 'theta', **kwargs) -> BasePrior .. py:function:: prior_from_args(name: str, args: Dict[str, Any], dims: Optional[Union[Tuple[str, Ellipsis], str]] = None) -> BasePrior .. py:data:: DEFAULT_PRIOR_ARGS .. py:data:: PM_DISTMAP