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declearn.model.sklearn.NumpyVector

Bases: Vector

Vector subclass to store numpy.ndarray coefficients.

This Vector is designed to store a collection of named numpy arrays or scalars, enabling computations that are either applied to each and every coefficient, or imply two sets of aligned coefficients (i.e. two NumpyVector instances with similar coefficients specifications).

Use vector.coefs to access the stored coefficients.

Notes

  • A NumpyVector can be operated with either a scalar value, or another NumpyVector that has similar specifications (same coefficient names, shapes and compatible dtypes).
  • Some other Vector classes might be made compatible with NumpyVector; in that case, operating with a NumpyVector will always result in a vector of the other type. This is notably the case with TensorflowVector and TorchVector.
  • There is currently no support for GPU-acceleration with the NumpyVector class, that only handles arrays and operations placed on a CPU device.
Source code in declearn/model/sklearn/_np_vec.py
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@register_vector_type(np.ndarray)
class NumpyVector(Vector):
    """Vector subclass to store numpy.ndarray coefficients.

    This Vector is designed to store a collection of named
    numpy arrays or scalars, enabling computations that are
    either applied to each and every coefficient, or imply
    two sets of aligned coefficients (i.e. two NumpyVector
    instances with similar coefficients specifications).

    Use `vector.coefs` to access the stored coefficients.

    Notes
    -----
    - A `NumpyVector` can be operated with either a scalar value,
      or another `NumpyVector` that has similar specifications
      (same coefficient names, shapes and compatible dtypes).
    - Some other `Vector` classes might be made compatible with
      `NumpyVector`; in that case, operating with a `NumpyVector`
      will always result in a vector of the other type. This is
      notably the case with `TensorflowVector` and `TorchVector`.
    - There is currently no support for GPU-acceleration with the
      `NumpyVector` class, that only handles arrays and operations
      placed on a CPU device.
    """

    @property
    def _op_add(self) -> Callable[[Any, Any], np.ndarray]:
        return np.add

    @property
    def _op_sub(self) -> Callable[[Any, Any], np.ndarray]:
        return np.subtract

    @property
    def _op_mul(self) -> Callable[[Any, Any], np.ndarray]:
        return np.multiply

    @property
    def _op_div(self) -> Callable[[Any, Any], np.ndarray]:
        return np.divide

    @property
    def _op_pow(self) -> Callable[[Any, Any], np.ndarray]:
        return np.power

    def __init__(
        self,
        coefs: Dict[str, np.ndarray],
    ) -> None:
        super().__init__(coefs)

    def __eq__(
        self,
        other: Any,
    ) -> bool:
        valid = isinstance(other, NumpyVector)
        if valid:
            valid = self.coefs.keys() == other.coefs.keys()
        if valid:
            valid = all(
                np.array_equal(self.coefs[k], other.coefs[k])
                for k in self.coefs
            )
        return valid

    def sign(
        self,
    ) -> Self:
        return self.apply_func(np.sign)

    def minimum(
        self,
        other: Union["Vector", float, ArrayLike],
    ) -> Self:
        if isinstance(other, NumpyVector):
            return self._apply_operation(other, np.minimum)
        return self.apply_func(np.minimum, other)

    def maximum(
        self,
        other: Union["Vector", float, ArrayLike],
    ) -> Self:
        if isinstance(other, Vector):
            return self._apply_operation(other, np.maximum)
        return self.apply_func(np.maximum, other)

    def sum(
        self,
        axis: Optional[int] = None,
        keepdims: bool = False,
    ) -> Self:
        coefs = {
            key: np.array(np.sum(val, axis=axis, keepdims=keepdims))
            for key, val in self.coefs.items()
        }
        return self.__class__(coefs)