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Commit f6c005cd authored by hhakim's avatar hhakim
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Add pyfaust.lazylinop.kron and LazyLinearOpKron associated class.

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wrapper/python/pyfaust/lazylinop.py
+ 103
− 46
View file @ f6c005cd
......@@ -120,8 +120,8 @@ class LazyLinearOp(LinearOperator):
Returns the LazyLinearOp transpose.
"""
self._checkattr('transpose')
new_op = self.__class__(init_lambda=lambda:
(self._lambda_stack()).transpose(),
new_op = LazyLinearOp(init_lambda=lambda:
(self.eval()).transpose(),
shape=(self.shape[1], self.shape[0]),
root_obj=self._root_obj)
return new_op
......@@ -138,8 +138,8 @@ class LazyLinearOp(LinearOperator):
Returns the LazyLinearOp conjugate.
"""
self._checkattr('conj')
new_op = self.__class__(init_lambda=lambda:
(self._lambda_stack()).conj(),
new_op = LazyLinearOp(init_lambda=lambda:
(self.eval()).conj(),
shape=self.shape,
root_obj=self._root_obj)
return new_op
......@@ -155,8 +155,8 @@ class LazyLinearOp(LinearOperator):
Returns the LazyLinearOp adjoint/transconjugate.
"""
self._checkattr('getH')
new_op = self.__class__(init_lambda=lambda:
(self._lambda_stack()).getH(),
new_op = LazyLinearOp(init_lambda=lambda:
(self.eval()).getH(),
shape=(self.shape[1], self.shape[0]),
root_obj=self._root_obj)
return new_op
......@@ -183,8 +183,8 @@ class LazyLinearOp(LinearOperator):
"""
self._checkattr('__add__')
new_op = self.__class__(init_lambda=lambda:
self._lambda_stack() + LazyLinearOp._eval_if_lazy(op),
new_op = LazyLinearOp(init_lambda=lambda:
self.eval() + LazyLinearOp._eval_if_lazy(op),
shape=(tuple(self.shape)),
root_obj=self._root_obj)
return new_op
......@@ -203,11 +203,11 @@ class LazyLinearOp(LinearOperator):
"""
Not Implemented self += op.
"""
raise NotImplementedError(self.__class__.__name__+".__iadd__")
raise NotImplementedError(LazyLinearOp.__name__+".__iadd__")
# can't do as follows, it recurses indefinitely because of self.eval
# self._checkattr('__iadd__')
# self = self.__class__(init_lambda=lambda:
# (self._lambda_stack()).\
# self = LazyLinearOp(init_lambda=lambda:
# (self.eval()).\
# __iadd__(LazyLinearOp._eval_if_lazy(op)),
# shape=(tuple(self.shape)),
# root_obj=self._root_obj)
......@@ -223,7 +223,7 @@ class LazyLinearOp(LinearOperator):
"""
self._checkattr('__sub__')
new_op = self.__class__(init_lambda=lambda: self._lambda_stack() - LazyLinearOp._eval_if_lazy(op),
new_op = LazyLinearOp(init_lambda=lambda: self.eval() - LazyLinearOp._eval_if_lazy(op),
shape=(tuple(self.shape)),
root_obj=self._root_obj)
return new_op
......@@ -237,9 +237,9 @@ class LazyLinearOp(LinearOperator):
"""
self._checkattr('__rsub__')
new_op = self.__class__(init_lambda=lambda:
new_op = LazyLinearOp(init_lambda=lambda:
LazyLinearOp._eval_if_lazy(op) -
self._lambda_stack(),
self.eval(),
shape=(tuple(self.shape)),
root_obj=self._root_obj)
return new_op
......@@ -248,11 +248,11 @@ class LazyLinearOp(LinearOperator):
"""
Not implemented self -= op.
"""
raise NotImplementedError(self.__class__.__name__+".__isub__")
raise NotImplementedError(LazyLinearOp.__name__+".__isub__")
# can't do as follows, it recurses indefinitely because of self.eval
# self._checkattr('__isub__')
# self = self.__class__(init_lambda=lambda:
# (self._lambda_stack()).\
# self = LazyLinearOp(init_lambda=lambda:
# (self.eval()).\
# __isub__(LazyLinearOp._eval_if_lazy(op)),
# shape=(tuple(self.shape)),
# root_obj=self._root_obj)
......@@ -268,8 +268,8 @@ class LazyLinearOp(LinearOperator):
"""
self._checkattr('__truediv__')
new_op = self.__class__(init_lambda=lambda:
self._lambda_stack() / LazyLinearOp._eval_if_lazy(op),
new_op = LazyLinearOp(init_lambda=lambda:
self.eval() / LazyLinearOp._eval_if_lazy(op),
shape=(tuple(self.shape)),
root_obj=self._root_obj)
return new_op
......@@ -278,12 +278,12 @@ class LazyLinearOp(LinearOperator):
"""
Not implemented self /= op.
"""
raise NotImplementedError(self.__class__.__name__+".__itruediv__")
raise NotImplementedError(LazyLinearOp.__name__+".__itruediv__")
# can't do as follows, it recurses indefinitely because of self.eval
#
# self._checkattr('__itruediv__')
# self = self.__class__(init_lambda=lambda:
# (self._lambda_stack()).\
# self = LazyLinearOp(init_lambda=lambda:
# (self.eval()).\
# __itruediv__(LazyLinearOp._eval_if_lazy(op)),
# shape=(tuple(self.shape)),
# root_obj=self._root_obj)
......@@ -302,13 +302,13 @@ class LazyLinearOp(LinearOperator):
raise TypeError('op must have a shape attribute')
if self.shape[1] != op.shape[0]:
raise ValueError('dimensions must agree')
if isinstance(op, LazyLinearOp):
res = self.__class__(init_lambda=lambda:
self.eval() @ op.eval(),
if isinstance(op, np.ndarray):
res = self.eval() @ op
else:
res = LazyLinearOp(init_lambda=lambda:
self.eval() @ LazyLinearOp._eval_if_lazy(op),
shape=(self.shape[0], op.shape[1]),
root_obj=self._root_obj)
else:
res = self.eval() @ op
return res
def dot(self, op):
......@@ -367,11 +367,11 @@ class LazyLinearOp(LinearOperator):
"""
Not implemented self @= op.
"""
raise NotImplementedError(self.__class__.__name__+".__imatmul__")
raise NotImplementedError(LazyLinearOp.__name__+".__imatmul__")
# can't do as follows, it recurses indefinitely because of self.eval
# self._checkattr('__imatmul__')
# self = self.__class__(init_lambda=lambda:
# (self._lambda_stack()).\
# self = LazyLinearOp(init_lambda=lambda:
# (self.eval()).\
# __imatmul__(LazyLinearOp._eval_if_lazy(op)),
# shape=(tuple(self.shape)),
# root_obj=self._root_obj)
......@@ -391,7 +391,7 @@ class LazyLinearOp(LinearOperator):
if self.shape[0] != op.shape[1]:
raise ValueError('dimensions must agree')
if isinstance(op, LazyLinearOp):
res = self.__class__(init_lambda=lambda:
res = LazyLinearOp(init_lambda=lambda:
op.eval() @ self.eval(),
shape=(self.shape[0], op.shape[1]),
root_obj=self._root_obj)
......@@ -414,8 +414,8 @@ class LazyLinearOp(LinearOperator):
self.shape == op.shape or \
op.shape[0] == 1 and op.shape[1] == self.shape[1] or \
op.shape[1] == 1 and op.shape[0] == self.shape[0]:
new_op = self.__class__(init_lambda=lambda:
self._lambda_stack() * LazyLinearOp._eval_if_lazy(op),
new_op = LazyLinearOp(init_lambda=lambda:
self.eval() * LazyLinearOp._eval_if_lazy(op),
shape=(tuple(self.shape)),
root_obj=self._root_obj)
return new_op
......@@ -436,9 +436,9 @@ class LazyLinearOp(LinearOperator):
op.shape[0] == 1 and op.shape[1] == self.shape[1] or \
op.shape[1] == 1 and op.shape[0] == self.shape[0]:
self._checkattr('__rmul__')
new_op = self.__class__(init_lambda=lambda:
new_op = LazyLinearOp(init_lambda=lambda:
LazyLinearOp._eval_if_lazy(op) *
self._lambda_stack(),
self.eval(),
shape=(tuple(self.shape)),
root_obj=self._root_obj)
return new_op
......@@ -449,11 +449,11 @@ class LazyLinearOp(LinearOperator):
"""
Not implemented self *= op.
"""
raise NotImplementedError(self.__class__.__name__+".__imul__")
raise NotImplementedError(LazyLinearOp.__name__+".__imul__")
# # can't do as follows, it recurses indefinitely because of self.eval
# self._checkattr('__imul__')
# self = self.__class__(init_lambda=lambda:
# (self._lambda_stack()).\
# self = LazyLinearOp(init_lambda=lambda:
# (self.eval()).\
# __imul__(LazyLinearOp._eval_if_lazy(op)),
# shape=(tuple(self.shape)),
# root_obj=self._root_obj)
......@@ -483,8 +483,8 @@ class LazyLinearOp(LinearOperator):
if isinstance(indices, tuple) and len(indices) == 2 and isinstance(indices[0], int) and isinstance(indices[1], int):
return self.eval().__getitem__(indices)
else:
return self.__class__(init_lambda=lambda:
(self._lambda_stack()).\
return LazyLinearOp(init_lambda=lambda:
(self.eval()).\
__getitem__(indices),
shape=self._newshape_getitem(indices),
root_obj=self._root_obj)
......@@ -582,8 +582,8 @@ class LazyLinearOp(LinearOperator):
for op in ops:
ncols += op.shape[1]
new_shape = (nrows, ncols)
new_op = self.__class__(init_lambda=lambda:
cat((self._lambda_stack(),
new_op = LazyLinearOp(init_lambda=lambda:
cat((self.eval(),
*[LazyLinearOp._eval_if_lazy(op) for op in
ops]), axis=axis),
shape=(new_shape),
......@@ -597,8 +597,8 @@ class LazyLinearOp(LinearOperator):
Returns the LazyLinearOp for real.
"""
self._checkattr('real')
new_op = self.__class__(init_lambda=lambda:
(self._lambda_stack()).real,
new_op = LazyLinearOp(init_lambda=lambda:
(self.eval()).real,
shape=self.shape,
root_obj=self._root_obj)
return new_op
......@@ -609,8 +609,8 @@ class LazyLinearOp(LinearOperator):
Returns the LazyLinearOp for imag.
"""
self._checkattr('imag')
new_op = self.__class__(init_lambda=lambda:
(self._lambda_stack()).imag,
new_op = LazyLinearOp(init_lambda=lambda:
(self.eval()).imag,
shape=self.shape,
root_obj=self._root_obj)
return new_op
......@@ -735,3 +735,60 @@ def vstack(tup):
return lop.concatenate(*tup[1:], axis=0)
else:
raise TypeError('lop must be a LazyLinearOp')
def kron(A, B):
return LazyLinearOpKron(lambda: A, A, B)
class LazyLinearOpKron(LazyLinearOp):
def __init__(self, init_lambda, A, B):
self.A = A
self.B = B
shape = (A.shape[0] * B.shape[0], A.shape[1] * B.shape[1])
super(LazyLinearOpKron, self).__init__(init_lambda, shape, A)
def conj(self):
return LazyLinearOpKron(self._lambda_stack, self.A.conj(), self.B.conj())
def transpose(self):
return LazyLinearOpKron(self._lambda_stack, self.A.T, self.B.T)
def getH(self):
return LazyLinearOpKron(self._lambda_stack, self.A.getH(), self.B.getH())
def __matmul__(self, op):
#TODO: refactor with parent function
self._checkattr('__matmul__')
if not hasattr(op, 'shape'):
raise TypeError('op must have a shape attribute')
if self.shape[1] != op.shape[0]:
raise ValueError('dimensions must agree')
if hasattr(op, 'reshape') and hasattr(op, '__matmul__') and hasattr(op,
'__getitem__'):
if op.ndim == 1:
op = op.reshape((op.size, 1))
one_dim = True
else:
one_dim = False
res = np.empty((self.shape[0], op.shape[1]))
for j in range(op.shape[1]):
op_mat = op[:, j].reshape((self.A.shape[1], self.B.shape[1]))
res[:, j] = (LazyLinearOp._eval_if_lazy(self.A) @ op_mat @
LazyLinearOp._eval_if_lazy(self.B).T).reshape(self.shape[0])
if one_dim:
res = res.ravel()
else:
res = LazyLinearOp(init_lambda=lambda:
self.eval() @ LazyLinearOp._eval_if_lazy(op),
shape=(self.shape[0], op.shape[1]),
root_obj=self._root_obj)
return res
def eval(self):
A = self.A
B = self.B
if not isinstance(A, np.ndarray):
A = A.toarray()
if not isinstance(B, np.ndarray):
B = B.toarray()
return np.kron(A, B)
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