Python chainers.Chainer类代码示例

class DeceptionAgent(MindAgent):
    def __init__(self):
        self.chainer = None
        print "Initialized DeceptionAgent."

    def create_chainer(self, atomspace, stimulate_atoms=False):
        self.chainer = Chainer(atomspace,
                               agent=self,
                               stimulateAtoms=stimulate_atoms,
                               allow_output_with_variables=False,
                               delete_temporary_variables=True)

        self.chainer.add_rule(
            ModusPonensRule(self.chainer, types.ImplicationLink))

    def run(self, atomspace):
        if self.chainer is None:
            self.create_chainer(atomspace)
            return

        result = self.chainer.forward_step()

        # if __VERBOSE__:
        #     print result

        return result

    def get_trails(self):
        return self.chainer.trails

    def get_history(self):
        return self.chainer.history.get_history()

class InferenceAgent(MindAgent):
    def __init__(self):
        self.chainer = None

    def create_chainer(self, atomspace, stimulate_atoms=True):
        self.chainer = Chainer(atomspace,
                               agent=self,
                               stimulateAtoms=stimulate_atoms,
                               allow_output_with_variables=False,
                               preferAttentionalFocus=True,
                               delete_temporary_variables=True)

        # ModusPonens:
        # Implication smokes(x) cancer(X)
        # smokes(Anna)
        # |= cancer(Anna)
        self.chainer.add_rule(
            ModusPonensRule(self.chainer, types.ImplicationLink))

    def run(self, atomspace):
        if self.chainer is None:
            self.create_chainer(atomspace)
            return

        if not check_result(atomspace):
            result = self.chainer.forward_step()
            return result

class InferenceAgent(MindAgent):
    def __init__(self):
        self.chainer = None

    def create_chainer(self, atomspace):
        self.chainer = Chainer(atomspace, stimulateAtoms=False)

        # Note:
        # The API for adding Rules and Links to a PLN Chainer is
        # hard to use. It could be redesigned so that you would
        # just pass a list of rule types and link types when you
        # instantiate the Chainer, without having to be aware of
        # the different sets of rules that require different
        # argument sets.

        for rule in RULES_CHAINER_LINKTYPE:
            for link_type in LINK_TYPES:
                print rule, link_type
                self.chainer.add_rule(rule(self.chainer, link_type))

        for rule in RULES_CHAINER:
            self.chainer.add_rule(rule(self.chainer))

    def run(self, atomspace):
        if self.chainer is None:
            self.create_chainer(atomspace)
            return

        result = self.chainer.forward_step()

        if __VERBOSE__:
            print result

        return result

def get_predicate_arguments(atomspace, predicate_name):
    """
    Finds the unique EvaluationLink for a predicate and returns its list
    of arguments.
    (Adapted from Alex's PLNUnitTester class)
    :param atomspace: the atomspace where the predicate should be looked for
    :param predicate_name: the name of the predicate
    :return: a list of the predicate arguments
    """
    chainer = Chainer(atomspace)
    var = chainer.new_variable()
    predicate = chainer.atomspace.add_node(types.PredicateNode, predicate_name)
    template = chainer.link(types.EvaluationLink,
                            [predicate, var])

    queries = chainer.lookup_atoms(template, "")
    # The template needs to be removed from the results
    queries.remove(template)
    if len(queries) != 1:
        if predicate_name == "undesired_outputs":
            return None
        else:
            raise ValueError("Predicate {0} must have 1 EvaluationLink"
                             .format(predicate_name))
    return queries[0].out[1].out

class InferenceAgent(MindAgent):
    def __init__(self):
        self.chainer = None
        print "Initializing InferenceAgent."

    def create_chainer(self, atomspace, stimulate_atoms=True):
        self.chainer = Chainer(atomspace,
                               agent=self,
                               stimulateAtoms=stimulate_atoms,
                               allow_output_with_variables=False,
                               preferAttentionalFocus=True,
                               delete_temporary_variables=True)

        # ModusPonens:
        # Implication smokes(x) cancer(X)
        # smokes(Anna)
        # |= cancer(Anna)
        self.chainer.add_rule(
            ModusPonensRule(self.chainer, types.ImplicationLink))

    def run(self, atomspace):
        if self.chainer is None:
            self.create_chainer(atomspace)
            print "PLN Chainer created."
            return

        print "PLN continuing."

        target = atomspace[scheme_eval_h(atomspace, "query")]

        if not check_result(atomspace):
            result = self.chainer.forward_step()
            self.chainer._give_stimulus(target, TARGET_STIMULUS)

            return result

class DeductionAgent(MindAgent):
    def __init__(self):
        self.chainer = None

    def create_chainer(self, atomspace):
        self.chainer = Chainer(atomspace, stimulateAtoms=False)

        link_types = [types.InheritanceLink]

        for link_type in link_types:
            self.chainer.add_rule(DeductionRule(self.chainer, link_type))

    def run(self, atomspace):
        # Run is invoked on every cogserver cognitive cycle
        # If it is the first time it has been invoked, then the chainer
        # needs to be created
        if self.chainer is None:
            self.create_chainer(atomspace)
            return

        result = self.chainer.forward_step()

        if __VERBOSE__:
            print result

        return result

    def get_trails(self):
        return self.chainer.trails

    def get_history(self):
        return self.chainer.history.get_history()

    def run_pln_example(self, filename):
        self.atomspace.clear()
        assert len(self.atomspace) == 0
            
        # Todo: The variable 'tmp' is not used
        tmp = open(filename, 'r')
        if not opencog.scheme_wrapper.load_scm(self.atomspace, filename):
            print 'unable to load file', filename
            return
        print filename

        chainer = Chainer(self.atomspace,
                          stimulateAtoms=False,
                          agent=self,
                          learnRuleFrequencies=False,
                          allow_output_with_variables=True,
                          allow_backchaining_with_variables=True,
                          check_cycles=True)

        try:
            queries = chainer.get_predicate_arguments('query')
        except ValueError, e:
            try:
                queries = chainer.get_predicate_arguments('queries')
            except ValueError, e:
                print e
                return

class InferenceAgent(MindAgent):
    def __init__(self):
        self.chainer = None

    def create_chainer(self, atomspace):
        self.chainer = Chainer(atomspace, stimulateAtoms=False)

        # EvaluationToMember, MemberToInheritance
        self.chainer.add_rule(EvaluationToMemberRule(self.chainer))
        self.chainer.add_rule(MemberToEvaluationRule(self.chainer))
        self.chainer.add_rule(MemberToInheritanceRule(self.chainer))
        self.chainer.add_rule(InheritanceToMemberRule(self.chainer))

        # For predicates with 2 arguments,
        # with the 0th argument made into a variable
        self.chainer.add_rule(
            GeneralEvaluationToMemberRule(self.chainer, 0, 2))

        # ModusPonens:
        # Implication smokes(x) cancer(X)
        # smokes(Anna)
        # |= cancer(Anna)
        self.chainer.add_rule(
            ModusPonensRule(self.chainer, types.ImplicationLink))

    def run(self, atomspace):
        if self.chainer is None:
            self.create_chainer(atomspace)
            return

        result = self.chainer.forward_step()
        return result

class InferenceAgent(MindAgent):
    def __init__(self):
        self.chainer = None
        self.query = None
        print "Initializing InferenceAgent."

    def create_chainer(self, atomspace, stimulate_atoms=True):
        """
        Creates the chainer for the "smokes" example. Optionally, you can
         define the target query before calling this method, by defining a
         Scheme expression named "query". For example, you can issue the
         Scheme expression "(define query hasCancer)" in reference to the
         predicate defined in smokes.scm before loading this agent. Once
         defined, the target query will receive stimulus at every time step.
         Stimulus requires the agent to be running in a CogServer.
         For a complete example that incorporates this behavior, see
         example.py here:
           https://github.com/opencog/external-tools/tree/master/attention
        """
        self.chainer = Chainer(atomspace,
                               agent=self,
                               stimulateAtoms=stimulate_atoms,
                               allow_output_with_variables=False,
                               preferAttentionalFocus=True,
                               delete_temporary_variables=True)

        # ModusPonens:
        # Implication smokes(x) cancer(X)
        # smokes(Anna)
        # |= cancer(Anna)
        self.chainer.add_rule(
            ModusPonensRule(self.chainer, types.ImplicationLink))

        # stimulateAtoms is only enabled when the agent is ran inside the
        # CogServer, since the functionality requires a CogServer and
        # attention allocation
        if self.chainer._stimulateAtoms:
            self.query = scheme_eval_h(atomspace, "query")

    def run(self, atomspace):
        if self.chainer is None:
            self.create_chainer(atomspace)
            print "PLN Chainer created."
            return

        print "PLN continuing."

        if not check_result(atomspace):
            result = self.chainer.forward_step()

            if self.query is not None:
                self.chainer._give_stimulus(atomspace[self.query],
                                            TARGET_STIMULUS)

            return result

class BackwardInferenceAgent(MindAgent):
    def __init__(self):
        self.chainer = None

    def create_chainer(self, atomspace):
        self.chainer = Chainer(atomspace, stimulateAtoms = False, agent = self)

        deduction_link_types = [types.InheritanceLink]
#            types.SubsetLink, types.IntensionalInheritanceLink]
        for link_type in deduction_link_types:
            self.chainer.add_rule(rules.InversionRule(self.chainer, link_type))
            self.chainer.add_rule(rules.DeductionRule(self.chainer, link_type))

#        self.chainer.add_rule(rules.NotCreationRule(self.chainer))
#        self.chainer.add_rule(rules.NotEliminationRule(self.chainer))

#        for rule in rules.create_and_or_rules(self.chainer, 1, 5):
#            self.chainer.add_rule(rule)

#        self.chainer.add_rule(EvaluationToMemberRule(self.chainer))
#        self.chainer.add_rule(MemberToInheritanceRule(self.chainer))

    def run(self, atomspace):
        # incredibly exciting futuristic display!
        #import os
        #os.system('cls' if os.name=='nt' else 'clear')

#        try:
        if self.chainer is None:
            self.create_chainer(atomspace)

        result = self.chainer.backward_step()
        if result:
            (outputs, inputs) = result

            if False:
                print '==== Inference ===='
                print show_atoms(outputs), '<=', show_atoms(inputs)

                print
                print '==== Attentional Focus ===='
                for atom in get_attentional_focus(atomspace)[0:30]:
                    print str(atom), atom.av

                #print '==== Result ===='
                #print output
                #print '==== Trail ===='
                #print_atoms( self.chainer.trails[output] )

            if self.chainer._all_nonzero_tvs(inputs):
                raw_input()

        else:
            print 'Invalid inference attempted'

class SyllogismAgent(MindAgent):
    def __init__(self):
        self.chainer = None

    def create_chainer(self, local_atomspace):
        self.chainer = Chainer(
            local_atomspace,
            agent=self,
            stimulateAtoms=False,
            # can be set to True to use AA on syllogisms
            preferAttentionalFocus=False,
            allow_output_with_variables=True,
            delete_temporary_variables=True,
        )
        # add rules here
        # self.chainer.add_rule()

    def run(self, local_atomspace):
        if self.chainer is None:
            self.create_chainer(local_atomspace)
            print ("PLN Chainer created.")
            return

        print ("PLN continuing.")

        result = self.chainer.forward_step()
        return result

    def create_chainer(self, atomspace):
        self.chainer = Chainer(atomspace, stimulateAtoms = False, agent = self, learnRuleFrequencies=True)

        # ImplicationLink is MixedImplicationLink, you could also have Extensional and Intensional Implication. etc. but that's a bit much.
        similarity_types = [types.SimilarityLink, types.ExtensionalSimilarityLink, types.IntensionalSimilarityLink]
#            types.EquivalenceLink]
        conditional_probability_types = [types.InheritanceLink, types.SubsetLink, types.IntensionalInheritanceLink,
            types.ImplicationLink]

        for link_type in conditional_probability_types:
            self.chainer.add_rule(rules.InversionRule(self.chainer, link_type))
            self.chainer.add_rule(rules.DeductionRule(self.chainer, link_type))
            self.chainer.add_rule(rules.ModusPonensRule(self.chainer, link_type))

        self.chainer.add_rule(rules.InheritanceRule(self.chainer))

        self.chainer.add_rule(rules.SimilarityRule(self.chainer))

        self.chainer.add_rule(rules.NotCreationRule(self.chainer))
        self.chainer.add_rule(rules.NotEliminationRule(self.chainer))

        for rule in rules.create_and_or_rules(self.chainer, 1, 5):
            self.chainer.add_rule(rule)

        self.chainer.add_rule(rules.SubsetEvaluationRule(self.chainer))
        self.chainer.add_rule(rules.InheritanceEvaluationRule(self.chainer))
        self.chainer.add_rule(rules.ExtensionalSimilarityEvaluationRule(self.chainer))

        self.chainer.add_rule(rules.EvaluationToMemberRule(self.chainer))
        self.chainer.add_rule(rules.MemberToInheritanceRule(self.chainer))
        self.chainer.add_rule(rules.MemberToSubsetRule(self.chainer))

        self.chainer.add_rule(rules.AttractionRule(self.chainer))

    def run(self, atomspace):
        if self.chainer is None:
            self.chainer = Chainer(atomspace, stimulateAtoms = True, agent = self)

            self.chainer.add_rule(rules.InversionRule(self.chainer))
            self.chainer.add_rule(rules.DeductionRule(self.chainer))

        # incredibly exciting futuristic display!
        #import os
        #os.system('cls' if os.name=='nt' else 'clear')

        try:
            result = self.chainer.forward_step()
            if result:
                (output, inputs) = result

                print '==== Inference ===='
                print output,str(output.av),'<=',' '.join(str(i)+str(output.av) for i in inputs)

                print
                print '==== Attentional Focus ===='
                for atom in get_attentional_focus(atomspace)[0:30]:
                    print str(atom), atom.av

                #print '==== Result ===='
                #print output
                #print '==== Trail ===='
                #print_atoms( self.chainer.trails[output] )
            else:
                print 'Invalid inference attempted'
        except Exception, e:
            print e

    def create_chainer(self, atomspace, stimulate_atoms=False):
        # if mode == 1 it is deception inference mode else it is deception
        # behavior mode
        if self.mode == 1:
            enable_variable_outputs = True
        else:
            enable_variable_outputs = False

        self.chainer = Chainer(atomspace,
                               agent=self,
                               stimulateAtoms=False,
                               preferAttentionalFocus=False,
                               allow_output_with_variables=enable_variable_outputs,
                               delete_temporary_variables=True)

        # Transformation rules
        # This rules are invoked randomly and are run in the background because,
        # they imply the creation of AndLinks between different sets of Links so
        # as to represent the existance of a state in the world or of state of knowledge
        # being simulated. In the ure this wouldn't be required as AndLink is used to denote
        # the existance of the required set of Links without the need of creating
        # the AndLink. If one wants to match AndLinked set of Links then the QuoteLink
        # can be used.(This is based on the assumption that ure doesn't make a fundamental
        # change on how the pattern matcher works presently)
        if self.mode == 1:
            self.chainer.add_rule(ImplicationAndRule(self.chainer))
        else:
            self.chainer.add_rule(AndCreationRule(self.chainer, 2))
            self.chainer.add_rule(AndCreationRule(self.chainer, 2, types.EvaluationLink))
            self.chainer.add_rule(AndCreationRule(self.chainer, 3, types.EvaluationLink))
            self.chainer.add_rule(ModusPonensRule(self.chainer, types.ImplicationLink))

def create_chainer(atomspace, rule_concept_nodes):
    """
    :param atomspace: the atomspace to be used by the chainer
    :param concept_nodes: list of ConceptNodes where the name is the name of a
    rule optionally amended by a link_type separated with ":", e.g.
    [ConceptNode "ModusPonensRule:ImplicationLink"]
    :return: the chainer that has been created
    """
    chainer = Chainer(atomspace,
                      stimulateAtoms=False,
                      allow_output_with_variables=True,
                      delete_temporary_variables=True)
    for rule_concept_node in rule_concept_nodes:
        rule_name, link_type = rule_concept_node.name.split(":")
        chainer.add_rule(rules[rule_name](chainer, link_types[link_type]))
    return chainer