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githart
opencog-python-restructure
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opencog-python-restructure
/
logic_frontend.py
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incremental-learner
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opencog-python-restructure
/
logic_frontend.py
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import
rpyc
from
pyopencog
.
atomspace
import
Atom
,
Handle
,
TruthValue
from
tree
import
tree_from_atom
,
atom_from_tree
,
FakeAtom
,
Tree
import
tree
# Attempt to run PLN code on PyPy using RPyC. Due to use of Cython it was quite messy (not transparent).
# Very fast, but mostly due to using FakeAtom anyway. There are still hard-to-find bugs and I think the easiest
# way is to just use pure Python wherever possible, rather than trying to use a transparent library that is not
# transparent in practice (due to conflicts with Cython).
def
rpyc_bc
(
atomspace
,
target
):
conn
=
rpyc
.
classic
.
connect
(
'localhost'
,
18813
)
c
=
conn
.
modules
[
'logic_backend'
].
Chainer
(
atomspace
)
print
c
.
rules
result_tree_list
=
c
.
bc
(
target
)
result_realtree_list
=
[
tree_with_real_atoms
(
tr
,
atomspace
)
for
tr
in
result_tree_list
]
tree
.
Atom
=
Atom
result_Atom_list
=
[
atom_from_tree
(
tr
,
atomspace
)
for
tr
in
result_realtree_list
]
for
(
tr
,
atom
)
in
zip
(
result_tree_list
,
result_Atom_list
):
for
tv
in
c
.
get_tvs
(
tr
):
atom
.
tv
=
TruthValue
(
tv
.
mean
,
tv
.
count
)
result_Handle_list
=
[
tr
.
h
for
tr
in
result_Atom_list
]
return
result_Handle_list
def
tree_with_real_atoms
(
tr
,
a
):
#if isinstance(tr.op, Atom):
if
isinstance
(
tr
.
op
,
FakeAtom
):
return
Atom
(
Handle
(
tr
.
op
.
_handle_value
),
a
)
elif
tr
.
is_leaf
():
return
tr
else
:
return
Tree
(
tr
.
op
, [
tree_with_real_atoms
(
child
,
a
)
for
child
in
tr
.
args
])
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