data = ["opencog/atomspace/core_types.scm", "opencog/scm/utilities.scm", "opencog/python/pln/examples/tuffy/smokes/smokes.scm"] if EXTRA_DATA: data.append("opencog/python/pln/examples/tuffy/smokes/extra-data.scm") for item in data: load_scm(atomspace, item) atoms = atomspace.get_atoms_by_type(types.Atom) for atom in atoms: print(atom) MAX_STEPS = 500 chainer = InferenceAgent() chainer.create_chainer(atomspace=atomspace, stimulate_atoms=False) answer = False outputs_produced = 0 for i in range(0, MAX_STEPS): result = chainer.run(atomspace) output = None input = None rule = None if result is not None: (rule, input, output) = result outputs_produced += 1
""" For testing smokes_agent.py without the cogserver """ from __future__ import print_function from opencog.atomspace import AtomSpace, TruthValue, types from data import * from pln.examples.tuffy.smokes.smokes_agent import InferenceAgent __author__ = 'Cosmo Harrigan' atoms = atomspace.get_atoms_by_type(types.Atom) for atom in atoms: print(atom) MAX_STEPS = 500 chainer = InferenceAgent() chainer.create_chainer(atomspace=atomspace) def check_result(): # Searches for EvaluationLinks where the first argument is: PredicateNode # "cancer" and the target of the predicate is a ConceptNode (representing a # person) eval_links = atomspace.get_atoms_by_type(types.EvaluationLink) num_results = 0 for eval_link in eval_links: out = [atom for atom in atomspace.get_outgoing(eval_link.h) if atom.is_a(types.PredicateNode) and atom.name == "cancer"] if out: list_link = atomspace.get_outgoing(eval_link.h)[1]
data = ["opencog/scm/core_types.scm", "opencog/scm/utilities.scm", "opencog/python/pln_old/examples/tuffy/smokes/smokes.scm"] if EXTRA_DATA: data.append("opencog/python/pln_old/examples/tuffy/smokes/extra-data.scm") for item in data: load_scm(atomspace, item) atoms = atomspace.get_atoms_by_type(types.Atom) for atom in atoms: print(atom) MAX_STEPS = 500 chainer = InferenceAgent() chainer.create_chainer(atomspace=atomspace, stimulate_atoms=False) answer = False outputs_produced = 0 for i in range(MAX_STEPS): result = chainer.run(atomspace) output = None input = None rule = None if result is not None: (rule, input, output) = result outputs_produced += 1
data = ["opencog/atomspace/core_types.scm", "opencog/scm/utilities.scm", "opencog/python/pln/examples/tuffy/smokes/smokes.scm"] if EXTRA_DATA: data.append("opencog/python/pln/examples/tuffy/smokes/extra-data.scm") for item in data: load_scm(atomspace, item) atoms = atomspace.get_atoms_by_type(types.Atom) for atom in atoms: print(atom) MAX_STEPS = 500 chainer = InferenceAgent() chainer.create_chainer(atomspace=atomspace) def check_result(): # Searches for EvaluationLinks where the first argument is: PredicateNode # "cancer" and the target of the predicate is a ConceptNode (representing a # person) eval_links = atomspace.get_atoms_by_type(types.EvaluationLink) num_results = 0 for eval_link in eval_links: out = [atom for atom in atomspace.get_outgoing(eval_link.h) if atom.is_a(types.PredicateNode) and atom.name == "cancer"] if out: list_link = atomspace.get_outgoing(eval_link.h)[1]