Source code for pylops.basicoperators.memoizeoperator

__all__ = ["MemoizeOperator"]

from typing import List, Tuple

import numpy as np

from pylops import LinearOperator
from pylops.utils.typing import NDArray


[docs]class MemoizeOperator(LinearOperator): r"""Memoize Operator. This operator can be used to wrap any PyLops operator and add a memoize functionality and stores the last ``max_neval`` model/data vector pairs Parameters ---------- Op : :obj:`pylops.LinearOperator` PyLops linear operator max_neval : :obj:`int`, optional Maximum number of previous evaluations stored, use ``np.inf`` for infinite memory Attributes ---------- shape : :obj:`tuple` Operator shape :math:`[n \times m]` explicit : :obj:`bool` Operator contains a matrix that can be solved explicitly (``True``) or not (``False``) """ def __init__( self, Op: LinearOperator, max_neval: int = 10, ) -> None: super().__init__(Op=Op) self.max_neval = max_neval self.store: List[Tuple[NDArray, NDArray]] = [] # Store a list of (x, y) self.neval = 0 # Number of evaluations of the operator def _matvec(self, x: NDArray) -> NDArray: for xstored, ystored in self.store: if np.allclose(xstored, x): return ystored if len(self.store) + 1 > self.max_neval: del self.store[0] # Delete oldest y = self.Op._matvec(x) self.neval += 1 self.store.append((x.copy(), y.copy())) return y def _rmatvec(self, y: NDArray) -> NDArray: for xstored, ystored in self.store: if np.allclose(ystored, y): return xstored if len(self.store) + 1 > self.max_neval: del self.store[0] # Delete oldest x = self.Op._rmatvec(y) self.neval += 1 self.store.append((x.copy(), y.copy())) return x