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Override linop instead of _evaluate_linop #818

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merged 3 commits into from
May 10, 2023
Merged

Override linop instead of _evaluate_linop #818

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5f5ba7b
Override `linop` instead of `_evaluate_linop`
timweiland May 4, 2023
ba3e3d1
Add suggestions from code review
timweiland May 9, 2023
18224cc
Merge branch 'main' into linop_array_subclass_compatibility
timweiland May 10, 2023
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12 changes: 5 additions & 7 deletions src/probnum/randprocs/covfuncs/_arithmetic_fallbacks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
import numpy as np

from probnum import linops, utils
from probnum.typing import NotImplementedType, ScalarLike
from probnum.typing import ArrayLike, NotImplementedType, ScalarLike

from ._covariance_function import BinaryOperandType, CovarianceFunction

Expand Down Expand Up @@ -55,8 +55,8 @@ def __init__(self, covfunc: CovarianceFunction, scalar: ScalarLike):
def _evaluate(self, x0: np.ndarray, x1: Optional[np.ndarray] = None) -> np.ndarray:
return self._scalar * self._covfunc(x0, x1)

def _evaluate_linop(
self, x0: np.ndarray, x1: Optional[np.ndarray]
def linop(
self, x0: ArrayLike, x1: Optional[ArrayLike] = None
) -> linops.LinearOperator:
return self._scalar * self._covfunc.linop(x0, x1)

Expand All @@ -82,7 +82,6 @@ class SumCovarianceFunction(CovarianceFunction):
"""

def __init__(self, *summands: CovarianceFunction):

if not all(
(summand.input_shape == summands[0].input_shape)
and (summand.output_shape_0 == summands[0].output_shape_0)
Expand All @@ -104,8 +103,8 @@ def _evaluate(self, x0: np.ndarray, x1: Optional[np.ndarray]) -> np.ndarray:
operator.add, (summand(x0, x1) for summand in self._summands)
)

def _evaluate_linop(
self, x0: np.ndarray, x1: Optional[np.ndarray]
def linop(
self, x0: ArrayLike, x1: Optional[ArrayLike] = None
) -> linops.LinearOperator:
return functools.reduce(
operator.add, (summand.linop(x0, x1) for summand in self._summands)
Expand Down Expand Up @@ -151,7 +150,6 @@ class ProductCovarianceFunction(CovarianceFunction):
"""

def __init__(self, *factors: CovarianceFunction):

if not all(
(factor.input_shape == factors[0].input_shape)
and (factor.output_shape_0 == factors[0].output_shape_0)
Expand Down