Examples with Term nars.language.Term used on opensource projects

Example 66 with Term

use of nars.language.Term in project opennars by opennars.

the class Counting method setEnabled.

@Override
public boolean setEnabled(NAR n, boolean enabled) {
    Memory memory = n.memory;
    if (obs == null) {
        obs = new EventObserver() {

            @Override
            public void event(Class event, Object[] a) {
                if ((event != Events.TaskDerive.class && event != Events.TaskAdd.class))
                    return;
                Task task = (Task) a[0];
                if (task.getPriority() < InternalExperience.MINIMUM_PRIORITY_TO_CREATE_WANT_BELIEVE_ETC) {
                    return;
                }
                if (task.sentence.punctuation == Symbols.JUDGMENT_MARK) {
                    // lets say we have <{...} --> M>.
                    if (task.sentence.term instanceof Inheritance) {
                        Inheritance inh = (Inheritance) task.sentence.term;
                        if (inh.getSubject() instanceof SetExt) {
                            SetExt set_term = (SetExt) inh.getSubject();
                            // this gets the cardinality of M
                            int cardinality = set_term.size();
                            // now create term <(*,M,cardinality) --> CARDINALITY>.
                            Term[] product_args = new Term[] { inh.getPredicate(), Term.get(cardinality) };
                            // TODO CARDINATLITY can be a static final instance shared by all
                            Term new_term = Inheritance.make(new Product(product_args), /* --> */
                            CARDINALITY);
                            if (new_term == null) {
                                // this usually happens when product_args contains the term CARDINALITY in which case it is an invalid Inheritance statement
                                return;
                            }
                            TruthValue truth = task.sentence.truth.clone();
                            Stamp stampi = task.sentence.stamp.clone();
                            Sentence j = new Sentence(new_term, Symbols.JUDGMENT_MARK, truth, stampi);
                            BudgetValue budg = task.budget.clone();
                            Task newTask = new Task(j, budg, true);
                            memory.addNewTask(newTask, "Derived (Cardinality)");
                        }
                    }
                }
            }
        };
    }
    memory.event.set(obs, enabled, Events.TaskDerive.class);
    return true;
}

Example 67 with Term

use of nars.language.Term in project opennars by opennars.

the class InternalExperience method beliefReason.

/**
 * used in full internal experience mode only
 */
protected void beliefReason(Sentence belief, Term beliefTerm, Term taskTerm, DerivationContext nal) {
    Memory memory = nal.memory;
    if (Memory.randomNumber.nextDouble() < INTERNAL_EXPERIENCE_RARE_PROBABILITY) {
        // the operators which dont have a innate belief
        // also get a chance to reveal its effects to the system this way
        Operator op = memory.getOperator(nonInnateBeliefOperators[Memory.randomNumber.nextInt(nonInnateBeliefOperators.length)]);
        Product prod = new Product(new Term[] { belief.term });
        if (op != null && prod != null) {
            Term new_term = Inheritance.make(prod, op);
            Sentence sentence = new Sentence(new_term, Symbols.GOAL_MARK, // a naming convension
            new TruthValue(1, Parameters.DEFAULT_JUDGMENT_CONFIDENCE), new Stamp(memory));
            float quality = BudgetFunctions.truthToQuality(sentence.truth);
            BudgetValue budget = new BudgetValue(Parameters.DEFAULT_GOAL_PRIORITY * INTERNAL_EXPERIENCE_PRIORITY_MUL, Parameters.DEFAULT_GOAL_DURABILITY * INTERNAL_EXPERIENCE_DURABILITY_MUL, quality);
            Task newTask = new Task(sentence, budget, true);
            nal.derivedTask(newTask, false, false, false);
        }
    }
    if (beliefTerm instanceof Implication && Memory.randomNumber.nextDouble() <= INTERNAL_EXPERIENCE_PROBABILITY) {
        Implication imp = (Implication) beliefTerm;
        if (imp.getTemporalOrder() == TemporalRules.ORDER_FORWARD) {
            // 1. check if its (&/,term,+i1,...,+in) =/> anticipateTerm form:
            boolean valid = true;
            if (imp.getSubject() instanceof Conjunction) {
                Conjunction conj = (Conjunction) imp.getSubject();
                if (!conj.term[0].equals(taskTerm)) {
                    // the expected needed term is not included
                    valid = false;
                }
                for (int i = 1; i < conj.term.length; i++) {
                    if (!(conj.term[i] instanceof Interval)) {
                        valid = false;
                        break;
                    }
                }
            } else {
                if (!imp.getSubject().equals(taskTerm)) {
                    valid = false;
                }
            }
            if (valid) {
                Operator op = memory.getOperator("^anticipate");
                if (op == null)
                    throw new RuntimeException(this + " requires ^anticipate operator");
                Product args = new Product(new Term[] { imp.getPredicate() });
                Term new_term = Operation.make(args, op);
                Sentence sentence = new Sentence(new_term, Symbols.GOAL_MARK, // a naming convension
                new TruthValue(1, Parameters.DEFAULT_JUDGMENT_CONFIDENCE), new Stamp(memory));
                float quality = BudgetFunctions.truthToQuality(sentence.truth);
                BudgetValue budget = new BudgetValue(Parameters.DEFAULT_GOAL_PRIORITY * INTERNAL_EXPERIENCE_PRIORITY_MUL, Parameters.DEFAULT_GOAL_DURABILITY * INTERNAL_EXPERIENCE_DURABILITY_MUL, quality);
                Task newTask = new Task(sentence, budget, true);
                nal.derivedTask(newTask, false, false, false);
            }
        }
    }
}

Example 68 with Term

use of nars.language.Term in project opennars by opennars.

the class TermTest method testUnconceptualizedTermInstancing.

@Test
public void testUnconceptualizedTermInstancing() throws Narsese.InvalidInputException {
    NAR n = new NAR();
    String term1String = "<a --> b>";
    Term term1 = np.parseTerm(term1String);
    Term term2 = np.parseTerm(term1String);
    assertTrue(term1.equals(term2));
    assertTrue(term1.hashCode() == term2.hashCode());
    CompoundTerm cterm1 = ((CompoundTerm) term1);
    CompoundTerm cterm2 = ((CompoundTerm) term2);
    // test subterms
    // 'a'
    assertTrue(cterm1.term[0].equals(cterm2.term[0]));
}

Example 69 with Term

use of nars.language.Term in project opennars by opennars.

the class TermTest method testConceptInstancing.

@Test
public void testConceptInstancing() throws Narsese.InvalidInputException {
    NAR n = new NAR();
    String statement1 = "<a --> b>.";
    Term a = np.parseTerm("a");
    assertTrue(a != null);
    Term a1 = np.parseTerm("a");
    assertTrue(a.equals(a1));
    n.addInput(statement1);
    n.cycles(4);
    n.addInput(" <a  --> b>.  ");
    n.cycles(1);
    n.addInput(" <a--> b>.  ");
    n.cycles(1);
    String statement2 = "<a --> c>.";
    n.addInput(statement2);
    n.cycles(4);
    Term a2 = np.parseTerm("a");
    assertTrue(a2 != null);
    Concept ca = n.memory.concept(a2);
    assertTrue(ca != null);
    assertEquals(true, n.memory.concepts.iterator().hasNext());
}

Example 70 with Term

use of nars.language.Term in project opennars by opennars.

the class Predict_NARS_Core method main.

public static void main(String[] args) throws Narsese.InvalidInputException, InterruptedException {
    Parameters.DEBUG = false;
    int duration = 4;
    float freq = 1.0f / duration * 0.03f;
    double discretization = 10;
    n = new NAR();
    n.param.noiseLevel.set(0);
    Random rnd = new Random();
    n.on(TaskImmediateProcess.class, new TaskImmediateProcess() {

        @Override
        public void onProcessed(Task t, DerivationContext n) {
            // return;
            if (t.sentence.getOccurenceTime() >= n.memory.time() && t.sentence.truth.getExpectation() > 0.5) {
                Term term = t.getTerm();
                int time = (int) t.sentence.getOccurenceTime() / thinkInterval;
                /*if(positionTruthExp.containsKey(time)) {
                        if(positionTruthExp.get(time) > t.sentence.truth.getExpectation()) {
                            return;
                        }
                    }*/
                int value = -1;
                String ts = term.toString();
            // Prediction(t.sentence, ts, time);
            }
        }
    });
    TreeMLData observed = new TreeMLData("value", Color.WHITE).setRange(0, 1f);
    // predictions = new TreeMLData[(int)discretization];
    predicted = new TreeMLData("seen", Color.WHITE).setRange(0, 1f);
    // for (int i = 0; i < predictions.length; i++) {
    // predictions[i] = new TreeMLData("Pred" + i,
    // Color.getHSBColor(0.25f + i / 4f, 0.85f, 0.85f));
    // }
    pred = (LineChart) new LineChart(predicted).thickness(16f).height(128).drawOverlapped();
    TimelineVis tc = new TimelineVis(pred, new LineChart(observed).thickness(16f).height(128).drawOverlapped());
    new NWindow("_", new PCanvas(tc)).show(800, 800, true);
    n.cycles((int) discretization * 4);
    NARSwing.themeInvert();
    new NARSwing(n);
    ChangedTextInput chg = new ChangedTextInput(n);
    int k = 0;
    String lastInput = "";
    boolean pause = false;
    HashSet<String> qus = new HashSet<String>();
    int truecnt = 0;
    String saved = "";
    int lastOrival = 0;
    while (true) {
        int steps = 40;
        int h = 0;
        do {
            truecnt++;
            if (truecnt % 100 == 0) {
            // qus.clear();
            }
            for (int i = 0; i < thinkInterval; i++) {
                n.cycles(1);
            }
            Thread.sleep(10);
            h++;
            int repeat = 500;
            signal = (float) Math.sin(freq * (k / 2 % 500) - Math.PI / 2) * 0.5f + 0.5f;
            int time = (int) (n.time() / thinkInterval);
            int val2 = (int) (((int) (((signal) * discretization)) * (10.0 / discretization)));
            // System.out.println("observed "+val);
            int tmp = val2;
            if (!pause && val2 != lastOrival) {
                float noise_amp = 0.5f;
                // noise
                val2 += (rnd.nextDouble() * noise_amp * 0.5f - noise_amp * 0.25f);
            }
            lastOrival = tmp;
            final int val = val2;
            lastInput = "<{" + val + "} --> value>. :|:";
            if (k % repeat == 0 && k != 0) {
                pause = true;
                // new run
                n.memory.seq_current.clear();
            }
            if (!pause && !saved.isEmpty()) {
                chg.set(saved);
                saved = "";
            }
            observed.add((int) time, val / 10.0);
            int curval = val;
            if (QUAnswers.containsKey(val)) {
                curval = QUAnswers.get(val);
            }
            int curtime = time;
            int hh = 0;
            // QUAnswers.put(8, 0); //end to start link is fixed
            while (QUAnswers.containsKey(curval)) {
                int shift = QUShift.get(curval) / thinkInterval;
                for (int i = 0; i < shift; i++) {
                    predicted.add((int) curtime + i, (curval) / 10.0);
                    pred.customColor.put(curtime + i, Color.RED.getRGB());
                    pred.customColor.put(curtime + i + 1, Color.RED.getRGB());
                }
                curtime = curtime + shift;
                curval = QUAnswers.get(curval);
                if (curval == 0) {
                    break;
                }
                hh++;
                if (hh > discretization * 2) {
                    break;
                }
            }
            // if(!positionTruthExp.containsKey(time)) { //keep pred line up to date
            // but don't overwrite predictions
            predicted.add((int) time, val / 10.0);
            if (true) {
                chg.set(lastInput);
                if (!pause) {
                } else {
                    saved = lastInput;
                }
                // n.addInput(lastInput);
                String S = "<(&/," + "<{" + val + "} --> value>,?I1) =/> ?what>";
                if (!qus.contains(S)) {
                    // n.addInput(S);
                    AnswerHandler cur = new AnswerHandler() {

                        @Override
                        public void onSolution(Sentence belief) {
                            // System.out.println("solution: " + belief);
                            System.out.println(belief);
                            String rpart = belief.toString().split("=/>")[1];
                            Prediction(belief, rpart, -1, val);
                        }
                    };
                    try {
                        // if(truecnt%10 == 0) {
                        qus.add(S);
                        n.askNow(S, cur);
                    // }
                    } catch (Narsese.InvalidInputException ex) {
                        Logger.getLogger(Predict_NARS_Core.class.getName()).log(Level.SEVERE, null, ex);
                    }
                }
            }
            if (h > 20) {
                pause = false;
            }
        } while (pause);
        k += steps;
    }
}