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opencog-python-restructure
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logic_frontend.py
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incremental-learner
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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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