Re: [sage-devel] Representation of LinearLayers in the crypto module
Well, this seems to be due to another part of the file, I got a MWE to work, so I guess I will figure it out. -- You received this message because you are subscribed to the Google Groups "sage-devel" group. To unsubscribe from this group and stop receiving emails from it, send an email to sage-devel+unsubscr...@googlegroups.com. To post to this group, send email to sage-devel@googlegroups.com. Visit this group at https://groups.google.com/group/sage-devel. For more options, visit https://groups.google.com/d/optout.
Re: [sage-devel] Representation of LinearLayers in the crypto module
Ah, this looks good!
Interestingly, while this works in the sage notebook, changing it in the
module results in an
sage: from sage.crypto.linearlayer import LinearLayerFactory
sage: LinearLayerFactory(GF(2))(MatrixSpace(GF(2), 2, 2), [1,0,0,1])
---
TypeError Traceback (most recent call last)
in ()
> 1 LinearLayerFactory(GF(Integer(2)))(MatrixSpace(GF(Integer(2)),
Integer(2), Integer(2)), [Integer(1),Integer(0),Integer(0),Integer(1)])
/home/asante/werkstatt/werkbank/sage/local/lib/python2.7/site-packages/sage/
crypto/linearlayer.pyc in LinearLayerFactory(K)
53 def LinearLayerFactory(K):
54 if K.characteristic() == 2 and K.degree() == 1:
---> 55 return type("LinearLayerGF2", (LinearLayer,
Matrix_mod2_dense), {})
56 if K.characteristic() == 2 and K.degree() >= 1:
57 return type("LinearLayerGF2E", (LinearLayer,
Matrix_gf2e_dense), {})
TypeError: metaclass conflict: the metaclass of a derived class must be a (
non-strict) subclass of the metaclasses of all its bases
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Re: [sage-devel] Representation of LinearLayers in the crypto module
Hi Friedrich,
I was thinking maybe something like this:
from sage.matrix.matrix_mod2_dense import Matrix_mod2_dense,
from sage.matrix.matrix_gf2e_dense import Matrix_gf2e_dense
class LinearLayer:
def foo(self):
return self[0, 1]
def LinearLayerFactory(K):
if K.characteristic() == 2 and K.degree() == 1:
return type("LinearLayerGF2", (Matrix_mod2_dense, LinearLayer), {})
if K.characteristic() == 2 and K.degree() == 1:
return type("LinearLayerGF2E", (Matrix_gf2e_dense, LinearLayer), {})
else:
raise NotImplementedError
T = LinearLayerFactory(GF(2))
T(MatrixSpace(GF(2), 2, 2), [0, 1, 2, 3], False, False).foo()
Cheers,
Martin
Friedrich Wiemer writes:
> Hi,
>
> I worked on an implementation of linear layers (basically a matrix over
> GF(2) or GF(2^n) with some special methods) in the crypto module during
> the sage days 94 and came up with this: #25735.
>
> Martin commented that it might make sense to just inherit from an
> appropriate matrix class, to avoid another layer of indirection. Seems a
> totally valid point for me, so I'm now trying to find out, what would be
> the appropriate inheritance. As we only need matrices over GF(2) or
> GF(2^n), I assume the correct super class would be their common super
> class Matrix_dense. But when I implement it, e.g. like this:
>
> %%cython
> cimport sage.matrix.matrix_dense as matrix_dense
> from sage.matrix.constructor import matrix
> cdef class LinearLayer(matrix_dense.Matrix_dense):
> cdef public object _m
>
> def __init__(self, parent, entries=None):
> m = matrix(parent, entries)
> self._m = m
>
> the matrix self._m "forgets" that it was a Matrix_mod2_dense or
> Matrix_gf2e_dense before:
>
> test_m = random_matrix(GF(2**4), 4, 4)
> test_m.__class__.__mro__
> (,
> ,
> ,
> ,
> ,
> ,
> ,
> ,
> ,
> )
> LinearLayer(test_m.parent(), test_m)
> test1._m.__class__.__mro__
> (,
> ,
> ,
> ,
> ,
> ,
> ,
> ,
> ,
> )
>
> Thus operations which are specialised in Matrix_mod2_dense or
> Matrix_gf2e_dense are not available anymore.
> Am I misunderstanding some concept here?
>
> Thanks in advance for your help,
> Friedrich
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[sage-devel] Representation of LinearLayers in the crypto module
Hi, I worked on an implementation of linear layers (basically a matrix over GF(2) or GF(2^n) with some special methods) in the crypto module during the sage days 94 and came up with this: #25735. Martin commented that it might make sense to just inherit from an appropriate matrix class, to avoid another layer of indirection. Seems a totally valid point for me, so I'm now trying to find out, what would be the appropriate inheritance. As we only need matrices over GF(2) or GF(2^n), I assume the correct super class would be their common super class Matrix_dense. But when I implement it, e.g. like this: %%cython cimport sage.matrix.matrix_dense as matrix_dense from sage.matrix.constructor import matrix cdef class LinearLayer(matrix_dense.Matrix_dense): cdef public object _m def __init__(self, parent, entries=None): m = matrix(parent, entries) self._m = m the matrix self._m "forgets" that it was a Matrix_mod2_dense or Matrix_gf2e_dense before: test_m = random_matrix(GF(2**4), 4, 4) test_m.__class__.__mro__ (, , , , , , , , , ) LinearLayer(test_m.parent(), test_m) test1._m.__class__.__mro__ (, , , , , , , , , ) Thus operations which are specialised in Matrix_mod2_dense or Matrix_gf2e_dense are not available anymore. Am I misunderstanding some concept here? Thanks in advance for your help, Friedrich -- You received this message because you are subscribed to the Google Groups "sage-devel" group. To unsubscribe from this group and stop receiving emails from it, send an email to sage-devel+unsubscr...@googlegroups.com. To post to this group, send email to sage-devel@googlegroups.com. Visit this group at https://groups.google.com/group/sage-devel. For more options, visit https://groups.google.com/d/optout.
