#include "macrovsa.hpp"
#include "time.hpp"
using namespace macrovsa;

/* Experiment corresponding to the (Viéville & Mercier 2024) draft
 * @param {bool} [with_RDFSS = true] Defines if we add extra RDFSs supplementary inferences.
 * @param {double} [tau_eat = 1] Defines if exact (tau_eat = 1) or approximate (tau_eat = 0.5) inference on eating.
 * @param {String} [trace = "ado"] Level of trace as defined Rules.hpp.
 */
void pizza_experiment(bool with_RDFSS = true, double tau_eat = 1, String trace = "ado")
{
  printf("pizza_experiment(bool with_RDFSS = %d, double tau_eat = %f, String trace = '%s'\n", with_RDFSS, tau_eat, trace.c_str());
  class SomeRules: public Rules {
public:
    SomeRules(String trace, bool with_RDFSS) : Rules(0, trace) {
      //
      // RDFS segment entailment rules
      //
      // ClassInheritance
      Rule_2(rdfs9,
             // getTau()
             return algo::conj(algo::sim(predicate_1, "rdf:type"), algo::sim(object_1, subject_2), algo::sim(predicate_2, "rdfs:subClassOf"));
             ,
             // setOutput()
             output.add(*subject_1, tau, "rdf:type", *object_2);
             );
      // ClassTransitivity
      Rule_2(rdfs11,
             // getTau()
             return algo::conj(algo::sim(predicate_1, "rdfs:subClassOf"), algo::sim(object_1, subject_2), algo::sim(predicate_2, "rdfs:subClassOf"));
             ,
             // setOutput()
             output.add(*subject_1, tau, "rdfs:subClassOf", *object_2);
             );
      // DomainInference
      Rule_2(rdfs2,
             // getTau()
             return algo::conj(algo::sim(predicate_1, subject_2), algo::sim(predicate_2, "rdfs:domain"));
             ,
             // setOutput()
             output.add(*subject_1, tau, "rdf:type", *object_2);
             );
      // RangeInference
      Rule_2(rdfs3,
             // getTau()
             return algo::conj(algo::sim(predicate_1, subject_2), algo::sim(predicate_2, "rdfs:range"));
             ,
             // setOutput()
             output.add(*object_1, tau, "rdf:type", *object_2);
             );
      // PropertyInference
      Rule_2(rdfs7,
             // getTau()
             return algo::conj(algo::sim(predicate_1, subject_2), algo::sim(predicate_2, "rdfs:subPropertyOf"));
             ,
             // setOutput()
             output.add(*subject_1, tau, *object_2, *object_1);
             );
      //
      // Additional entailment rules to infer node types
      // - Note: this is mainly used to examplify and test Rule_1 and Rule_3 macro and multiple outputs
      //
      if(with_RDFSS) {
        // PropertyTypeInference
        Rule_1(rdf1,
               // getTau()
               return 1;
               ,
               // setOutput()
               output.add(*predicate_1, "rdf:type", "rdfs:Property");
               );
        // ClassTypeInference
        Rule_1(rdfss1,
               // getTau()
               return algo::sim(predicate_1, "rdf:type");
               ,
               // setOutput()
               output.add(*object_1, tau, "rdf:type", "rdfs:Class");
               );
        Rule_1(rdfss2,
               // getTau()
               return algo::sim(predicate_1, "rdfs:subClassOf");
               ,
               // setOutput()
               output.add(*subject_1, tau, "rdf:type", "rdfs:Class");
               output.add(*object_1, tau, "rdf:type", "rdfs:Class");
               );
        Rule_1(rdfss3,
               // getTau()
               return algo::sim(predicate_1, "rdfs:subPropertyOf");
               ,
               // setOutput()
               output.add(*subject_1, tau, "rdf:type", "rdfs:Property");
               output.add(*object_1, tau, "rdf:type", "rdfs:Property");
               );
        // PropertyClassInheritance
        Rule_3(rdfss9,
               // getTau()
               return algo::conj(algo::sim(predicate_1, "rdf:type"), algo::sim(object_1, subject_2), algo::sim(predicate_2, subject_3), algo::sim(predicate_3, "rdf:type"), algo::sim(object_3, "rdfs:Property"));
               ,
               // setOutput()
               output.add(*subject_1, tau, *predicate_2, *object_2);
               );
      }
    }
  }
  someRules(trace, with_RDFSS);

  // Defines incoming facts
  RelationalMap incoming("Luigi pizza incomimg facts");
  incoming.add("Luigi", tau_eat, "eats", "thisPizza");
  incoming.add("eats", "rdfs:domain", "Person");
  incoming.add("eats", "rdfs:range", "Food");
  incoming.add("thisPizza", "rdf:type", "MagheritaPizza");
  incoming.add("thisPizza", "hasTopping", "thisMozzarella");
  incoming.add("MagheritaPizza", "rdfs:subClassOf", "Pizza");
  incoming.add("Pizza", "rdfs:subClassOf", "Food");
  incoming.add("hasTopping", "rdfs:subPropertyOf", "hasIngredient");
  incoming.add("MagheritaPizza", "hasTopping", "Tomato");

  // Runs inference
  RelationalMap infered("Luigi pizza infered facts");
  aidesys::now(false, false);
  someRules.apply(infered, incoming);
  printf("Calculus duration in msec : %.3f\n", aidesys::now(false, true));
  printf("%s\n", infered.asString().c_str());
}
int main()
{
  pizza_experiment(false, 1, "ado");
  pizza_experiment(false, 1, "");
  pizza_experiment(false, 0.5, "");
  pizza_experiment(true, 1, "");
  return 0;
}