import numpy as np
from pylops import LinearOperator
[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, max_neval=10):
self.Op = Op
self.shape = Op.shape
self.dtype = np.dtype(Op.dtype)
self.explicit = False
self.max_neval = max_neval
self.store = [] # Store a list of Tuples (x, y)
self.neval = 0 # Number of evaluations of the operator
def _matvec(self, x):
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):
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