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: 1.15.0 preliminary _distribution_infrastructure
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# mypy: disable-error-code="explicit-override" | ||
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import abc | ||
from collections.abc import Mapping, Sequence, Set as AbstractSet | ||
from typing import Any, ClassVar, Final, Literal as L, TypeAlias, overload | ||
from typing_extensions import LiteralString, TypeVar, override | ||
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import numpy as np | ||
import optype.numpy as onp | ||
import optype.typing as opt | ||
from scipy._typing import ToRNG | ||
from ._probability_distribution import _BaseDistribution | ||
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# TODO: | ||
# `__all__ = ["Mixture", "abs", "exp", "log", "make_distribution", "order_statistic", "truncate"] | ||
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_ValidationPolicy: TypeAlias = L["skip_all"] | None | ||
_CachePolicy: TypeAlias = L["no_cache"] | None | ||
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_FloatT = TypeVar("_FloatT", bound=np.floating[Any]) | ||
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### | ||
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# TODO(jorenham): Generic dtype | ||
class _Domain(abc.ABC): | ||
symbols: ClassVar[Mapping[float, LiteralString]] | ||
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@abc.abstractmethod | ||
@override | ||
def __str__(self, /) -> str: ... | ||
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# | ||
@abc.abstractmethod | ||
def contains(self, /, x: onp.ArrayND[Any]) -> onp.ArrayND[np.bool_]: ... | ||
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# | ||
@abc.abstractmethod | ||
def draw(self, /, n: int) -> onp.ArrayND[np.float64]: ... | ||
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# | ||
@abc.abstractmethod | ||
def get_numerical_endpoints( | ||
self, | ||
/, | ||
x: Mapping[str, onp.ToFloat | onp.ToFloatND], | ||
) -> tuple[onp.ArrayND[np.float64], onp.ArrayND[np.float64]]: ... | ||
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# TODO(jorenham): Generic dtype | ||
class _SimpleDomain(_Domain, metaclass=abc.ABCMeta): | ||
def __init__(self, /, endpoints: tuple[float, float] = ..., inclusive: tuple[bool, bool] = (False, False)) -> None: ... | ||
@override | ||
def __str__(self, /) -> str: ... # noqa: PYI029 | ||
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# | ||
def define_parameters(self, /, *parameters: _Parameter) -> None: ... | ||
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# | ||
@override | ||
def get_numerical_endpoints( # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride] | ||
self, | ||
/, | ||
parameter_values: Mapping[str, onp.ToFloat | onp.ToFloatND], | ||
) -> tuple[onp.ArrayND[np.float64], onp.ArrayND[np.float64]]: ... | ||
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# | ||
@override | ||
def contains( # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride] | ||
self, | ||
/, | ||
item: onp.ArrayND[np.integer[Any] | np.floating[Any]], | ||
parameter_values: Mapping[str, onp.ToFloat | onp.ToFloatND] | None = None, | ||
) -> onp.ArrayND[np.bool_]: ... | ||
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class _RealDomain(_SimpleDomain): | ||
@override | ||
def draw( # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride] | ||
self, | ||
/, | ||
n: int, | ||
type_: L["in", "out", "on", "nan"], | ||
min: onp.ArrayND[np.floating[Any]], | ||
max: onp.ArrayND[np.floating[Any]], | ||
squeezed_base_shape: tuple[int, ...], | ||
rng: ToRNG = None, | ||
) -> onp.ArrayND[np.float64]: ... | ||
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# | ||
class _Parameter(abc.ABC): | ||
def __init__( | ||
self, | ||
/, | ||
name: str, | ||
*, | ||
domain: _Domain, | ||
symbol: str | None = None, | ||
typical: _Domain | tuple[int | str, int | str] | None = None, | ||
) -> None: ... | ||
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# | ||
def draw( | ||
self, | ||
/, | ||
size: tuple[int, ...] | None = None, | ||
*, | ||
rng: ToRNG = None, | ||
region: L["domain", "typical"] = "domain", | ||
proportions: tuple[onp.ToFloat, onp.ToFloat, onp.ToFloat, onp.ToFloat] | None = None, | ||
parameter_values: Mapping[str, onp.ToFloat | onp.ToFloatND] | None = None, | ||
) -> onp.ArrayND[np.float64]: ... | ||
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# | ||
@overload | ||
@abc.abstractmethod | ||
def validate(self, /, arr: onp.ToFloat) -> tuple[_FloatT, np.dtype[_FloatT], onp.Array0D[np.bool_]]: ... | ||
@overload | ||
@abc.abstractmethod | ||
def validate(self, /, arr: onp.ToFloatND) -> tuple[onp.ArrayND[_FloatT], np.dtype[_FloatT], onp.ArrayND[np.bool_]]: ... | ||
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class _RealParameter(_Parameter): | ||
@overload # type: ignore[override] | ||
def validate( | ||
self, | ||
/, | ||
arr: onp.ToFloat, | ||
parameter_values: Mapping[str, onp.ToFloat | onp.ToFloatND], | ||
) -> tuple[_FloatT, np.dtype[_FloatT], onp.Array0D[np.bool_]]: ... | ||
@overload | ||
def validate( # pyright: ignore[reportIncompatibleMethodOverride] | ||
self, | ||
/, | ||
arr: onp.ToFloatND, | ||
parameter_values: Mapping[str, onp.ToFloat | onp.ToFloatND], | ||
) -> tuple[onp.ArrayND[_FloatT], np.dtype[_FloatT], onp.ArrayND[np.bool_]]: ... | ||
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class _Parameterization: | ||
parameters: Final[Mapping[str, _Parameter]] | ||
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def __init__(self, /, *parameters: _Parameter) -> None: ... | ||
def __len__(self, /) -> int: ... | ||
def copy(self, /) -> _Parameterization: ... | ||
def matches(self, /, parameters: AbstractSet[str]) -> bool: ... | ||
def validation( | ||
self, | ||
/, | ||
parameter_values: Mapping[str, _Parameter], | ||
) -> tuple[onp.ArrayND[np.bool] | np.dtype[np.floating[Any]]]: ... | ||
def draw( | ||
self, | ||
/, | ||
sizes: tuple[int, ...] | Sequence[tuple[int, ...]] | None = None, | ||
rng: ToRNG = None, | ||
proportions: tuple[onp.ToFloat, onp.ToFloat, onp.ToFloat, onp.ToFloat] | None = None, | ||
region: L["domain", "typical"] = "domain", | ||
) -> dict[str, onp.ArrayND[np.float64]]: ... | ||
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### | ||
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class ContinuousDistribution(_BaseDistribution): | ||
def __init__( | ||
self, | ||
/, | ||
*, | ||
tol: opt.Just[float], | ||
validation_policy: _ValidationPolicy = None, | ||
cache_policy: _CachePolicy = None, | ||
) -> None: ... | ||
def reset_cache(self, /) -> None: ... | ||
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class TransformedDistribution(ContinuousDistribution): | ||
# TODO(jorenham) | ||
... | ||
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class TruncatedDistribution(TransformedDistribution): | ||
# TODO(jorenham) | ||
... | ||
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class ShiftedScaledDistribution(TransformedDistribution): | ||
# TODO(jorenham) | ||
... | ||
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class OrderStatisticDistribution(TransformedDistribution): | ||
# TODO(jorenham) | ||
... | ||
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class MonotonicTransformedDistribution(TransformedDistribution): | ||
# TODO(jorenham) | ||
... | ||
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class FoldedDistribution(TransformedDistribution): | ||
# TODO(jorenham) | ||
... | ||
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class Mixture(_BaseDistribution): | ||
# TODO(jorenham) | ||
... |