Examples with Sentence nars.entity.Sentence used on opensource projects

Example 1 with Sentence

use of nars.entity.Sentence in project opennars by opennars.

the class PerceptionAccel method perceive.

public void perceive(DerivationContext nal) {
    // implement Peis idea here now
    // we start with length 2 compounds, and search for patterns which are one longer than the longest observed one
    boolean longest_result_derived_already = false;
    for (int Len = cur_maxlen + 1; Len >= 2; Len--) {
        // ok, this is the length we have to collect, measured from the end of event buffer
        // there is a interval term for every event
        Term[] relterms = new Term[2 * Len - 1];
        // measuring its distance to the next event, but for the last event this is obsolete
        // thus it are 2*Len-1] terms
        Task newEvent = eventbuffer.get(eventbuffer.size() - 1);
        TruthValue truth = newEvent.sentence.truth;
        Stamp st = new Stamp(nal.memory);
        ArrayList<Long> evBase = new ArrayList<Long>();
        int k = 0;
        for (int i = 0; i < Len; i++) {
            // we go till to the end of the event buffer
            int j = eventbuffer.size() - 1 - (Len - 1) + i;
            if (j < 0) {
                // but the mechanism already looks for length 2 patterns on the occurence of the first event
                break;
            }
            Task current = eventbuffer.get(j);
            for (long l : current.sentence.stamp.evidentialBase) {
                evBase.add(l);
            }
            relterms[k] = current.sentence.term;
            if (i != Len - 1) {
                // if its not the last one, then there is a next one for which we have to put an interval
                truth = TruthFunctions.deduction(truth, current.sentence.truth);
                Task next = eventbuffer.get(j + 1);
                relterms[k + 1] = new Interval(next.sentence.getOccurenceTime() - current.sentence.getOccurenceTime());
            }
            k += 2;
        }
        long[] evB = new long[evBase.size()];
        int u = 0;
        for (long l : evBase) {
            evB[u] = l;
            u++;
        }
        st.baseLength = evB.length;
        st.evidentialBase = evB;
        boolean eventBufferDidNotHaveSoMuchEvents = false;
        for (int i = 0; i < relterms.length; i++) {
            if (relterms[i] == null) {
                eventBufferDidNotHaveSoMuchEvents = true;
            }
        }
        if (eventBufferDidNotHaveSoMuchEvents) {
            continue;
        }
        // decide on the tense of &/ by looking if the first event happens parallel with the last one
        // Todo refine in 1.6.3 if we want to allow input of difference occurence time
        boolean after = newEvent.sentence.stamp.after(eventbuffer.get(eventbuffer.size() - 1 - (Len - 1)).sentence.stamp, Parameters.DURATION);
        // critical part: (not checked for correctness yet):
        // we now have to look at if the first half + the second half already exists as concept, before we add it
        Term[] firstHalf;
        Term[] secondHalf;
        if (relterms[Len - 1] instanceof Interval) {
            // the middle can be a interval, for example in case of a,+1,b , in which case we dont use it
            // so we skip the middle here
            firstHalf = new Term[Len - 1];
            // as well as here
            secondHalf = new Term[Len - 1];
            // make index mapping easier by counting
            int h = 0;
            for (int i = 0; i < Len - 1; i++) {
                firstHalf[i] = relterms[h];
                h++;
            }
            // we have to overjump the middle element this is why
            h += 1;
            for (int i = 0; i < Len - 1; i++) {
                secondHalf[i] = relterms[h];
                h++;
            }
        } else {
            // it is a event so its fine
            // 2*Len-1 in total
            firstHalf = new Term[Len];
            // but the middle is also used in the second one
            secondHalf = new Term[Len];
            // make index mapping easier by counting
            int h = 0;
            for (int i = 0; i < Len; i++) {
                firstHalf[i] = relterms[h];
                h++;
            }
            // we have to use the middle twice this is why
            h--;
            for (int i = 0; i < Len; i++) {
                secondHalf[i] = relterms[h];
                h++;
            }
        }
        Term firstC = Conjunction.make(firstHalf, after ? ORDER_FORWARD : ORDER_CONCURRENT);
        Term secondC = Conjunction.make(secondHalf, after ? ORDER_FORWARD : ORDER_CONCURRENT);
        Concept C1 = nal.memory.concept(firstC);
        Concept C2 = nal.memory.concept(secondC);
        if (C1 == null || C2 == null) {
            if (debugMechanism) {
                System.out.println("one didn't exist: " + firstC.toString() + " or " + secondC.toString());
            }
            // the components were not observed, so don't allow creating this compound
            continue;
        }
        if (C1.getPriority() < partConceptsPrioThreshold || C2.getPriority() < partConceptsPrioThreshold) {
            // too less priority
            continue;
        }
        Conjunction C = (Conjunction) Conjunction.make(relterms, after ? ORDER_FORWARD : ORDER_CONCURRENT);
        // importance "summation"
        Sentence S = new Sentence(C, Symbols.JUDGMENT_MARK, truth, st);
        Task T = new Task(S, new BudgetValue(BudgetFunctions.or(C1.getPriority(), C2.getPriority()), Parameters.DEFAULT_JUDGMENT_DURABILITY, truth), true);
        if (debugMechanism) {
            System.out.println("success: " + T.toString());
        }
        if (longest_result_derived_already) {
            T.setElemOfSequenceBuffer(false);
        }
        longest_result_derived_already = true;
        // lets make the new event the parent task, and derive it
        nal.derivedTask(T, false, false, false);
    }
}

Example 2 with Sentence

use of nars.entity.Sentence in project opennars by opennars.

the class GlobalAnticipation method temporalPredictionsAdapt.

// check all predictive statements, match them with last events
public void temporalPredictionsAdapt(DerivationContext nal) {
    if (TEMPORAL_PREDICTION_FEEDBACK_ACCURACY_DIV == 0.0f) {
        return;
    }
    ArrayList<Task> lastEvents = new ArrayList<Task>();
    for (Task stmLast : stm) {
        lastEvents.add(stmLast);
    }
    if (lastEvents.isEmpty()) {
        return;
    }
    final long duration = Parameters.DURATION;
    ArrayList<Task> derivetasks = new ArrayList<Task>();
    for (final Task c : current_tasks) {
        // a =/> b or (&/ a1...an) =/> b
        boolean concurrent_conjunction = false;
        Term[] args = new Term[1];
        Implication imp = (Implication) c.sentence.term.clone();
        boolean concurrent_implication = imp.getTemporalOrder() == TemporalRules.ORDER_CONCURRENT;
        args[0] = imp.getSubject();
        if (imp.getSubject() instanceof Conjunction) {
            Conjunction conj = (Conjunction) imp.getSubject();
            if (!conj.isSpatial) {
                if (conj.temporalOrder == TemporalRules.ORDER_FORWARD || conj.temporalOrder == TemporalRules.ORDER_CONCURRENT) {
                    concurrent_conjunction = conj.temporalOrder == TemporalRules.ORDER_CONCURRENT;
                    // in case of &/ this are the terms
                    args = conj.term;
                }
            }
        }
        int i = 0;
        boolean matched = true;
        int off = 0;
        long expected_time = lastEvents.get(0).sentence.getOccurenceTime();
        for (i = 0; i < args.length; i++) {
            // handling of intervals:
            if (args[i] instanceof Interval) {
                if (!concurrent_conjunction) {
                    expected_time += ((Interval) args[i]).time;
                }
                off++;
                continue;
            }
            if (i - off >= lastEvents.size()) {
                break;
            }
            if (!Variables.hasSubstitute(Symbols.VAR_INDEPENDENT, args[i], lastEvents.get(i - off).sentence.term)) {
                // it didnt match, instead sth different unexpected happened
                // whether intermediate events should be tolerated or not was a important question when considering this,
                matched = false;
                // if it should be allowed, the sequential match does not matter only if the events come like predicted.
                break;
            } else {
                if (lastEvents.get(i - off).sentence.truth.getExpectation() <= 0.5f) {
                    // it matched according to sequence, but is its expectation bigger than 0.5? todo: decide how truth values of the expected events
                    // it didn't happen
                    matched = false;
                    break;
                }
                long occurence = lastEvents.get(i - off).sentence.getOccurenceTime();
                boolean right_in_time = Math.abs(occurence - expected_time) < ((double) duration) / TEMPORAL_PREDICTION_FEEDBACK_ACCURACY_DIV;
                if (!right_in_time) {
                    // it matched so far, but is the timing right or did it happen when not relevant anymore?
                    matched = false;
                    break;
                }
            }
            if (!concurrent_conjunction) {
                expected_time += duration;
            }
        }
        if (concurrent_conjunction && !concurrent_implication) {
            // implication is not concurrent
            // so here we have to add duration
            expected_time += duration;
        } else if (!concurrent_conjunction && concurrent_implication) {
            expected_time -= duration;
        }
        // ok it matched, is the consequence also right?
        if (matched && lastEvents.size() > args.length - off) {
            long occurence = lastEvents.get(args.length - off).sentence.getOccurenceTime();
            boolean right_in_time = Math.abs(occurence - expected_time) < ((double) duration) / TEMPORAL_PREDICTION_FEEDBACK_ACCURACY_DIV;
            if (right_in_time && Variables.hasSubstitute(Symbols.VAR_INDEPENDENT, imp.getPredicate(), lastEvents.get(args.length - off).sentence.term)) {
                // it matched and same consequence, so positive evidence
                // c.sentence.truth=TruthFunctions.revision(c.sentence.truth, new TruthValue(1.0f,Parameters.DEFAULT_JUDGMENT_CONFIDENCE));
                Sentence s2 = new Sentence(c.sentence.term.clone(), Symbols.JUDGMENT_MARK, new TruthValue(1.0f, Parameters.DEFAULT_JUDGMENT_CONFIDENCE), new Stamp(nal.memory));
                Task t = new Task(s2, new BudgetValue(Parameters.DEFAULT_JUDGMENT_PRIORITY, Parameters.DEFAULT_JUDGMENT_DURABILITY, s2.truth), true);
                derivetasks.add(t);
            } else {
                // it matched and other consequence, so negative evidence
                // c.sentence.truth=TruthFunctions.revision(c.sentence.truth, new TruthValue(0.0f,Parameters.DEFAULT_JUDGMENT_CONFIDENCE));
                Sentence s2 = new Sentence(c.sentence.term.clone(), Symbols.JUDGMENT_MARK, new TruthValue(0.0f, Parameters.DEFAULT_JUDGMENT_CONFIDENCE), new Stamp(nal.memory));
                Task t = new Task(s2, new BudgetValue(Parameters.DEFAULT_JUDGMENT_PRIORITY, Parameters.DEFAULT_JUDGMENT_DURABILITY, s2.truth), true);
                derivetasks.add(t);
            }
        // todo use derived task with revision instead
        }
    }
    for (Task t : derivetasks) {
        if (nal.derivedTask(t, false, false, false)) {
            boolean debug = true;
        }
    }
    ArrayList<Task> toDelete = new ArrayList<Task>();
    for (Task t : current_tasks) {
        Concept w = nal.memory.concept(t.sentence.term);
        if (w == null) {
            // concept does not exist anymore, delete
            toDelete.add(t);
        }
    }
    for (Task t : toDelete) {
        current_tasks.remove(t);
    }
}

Example 3 with Sentence

use of nars.entity.Sentence in project opennars by opennars.

the class SymRecognizer method jButton3ActionPerformed.

private void jButton3ActionPerformed(java.awt.event.ActionEvent evt) {
    // GEN-FIRST:event_jButton3ActionPerformed
    resetDetection();
    StringBuilder build = new StringBuilder();
    StringBuilder build2 = new StringBuilder();
    build2.append("<(&|,");
    for (int x = 0; x < SZ; x += 1) {
        for (int y = 0; y < SZ; y += 1) {
            int used_X = x;
            int used_Y = y;
            Color col1 = new Color(canvasIMG.getRGB(x, y));
            float col = ((float) col1.getRed()) / 255.0f;
            if (col > 0.0) {
                float freq = 0.5f + (col - 0.5f);
                if (invar.isSelected()) {
                    build.append("<p[" + String.valueOf(used_X) + "," + String.valueOf(used_Y) + "] --> [on]>. :|: %" + String.valueOf(freq) + "%");
                    build.append("\n");
                    build2.append("<p[" + String.valueOf(used_X) + "," + String.valueOf(used_Y) + "] --> [on]>,");
                } else {
                    build.append("<p_" + String.valueOf(used_X) + "_" + String.valueOf(used_Y) + " --> [on]>. :|: %" + String.valueOf(freq) + "%");
                    build.append("\n");
                    build2.append("<p_" + String.valueOf(used_X) + "_" + String.valueOf(used_Y) + " --> [on]>,");
                }
            }
        }
    }
    String s2 = build2.toString();
    s2 = s2.substring(0, s2.length() - 1);
    s2 = s2 + ")";
    inputPanel.setText(build.toString());
    if (evt == null) {
        String question = "<{?what} --> [observed]>?";
        additional[exid] = s2 + " ==> <{example" + exid + "} --> [observed]>>.";
        inputPanel2.setText(additional[exid] + "\n" + question);
    } else {
        nar = new NAR();
        if (invar1.isSelected()) {
            gui = new NARSwing(nar);
        }
        int u = 0;
        inputPanel2.setText("");
        // for(String s : questions) {
        String s = question;
        {
            if (s != null) {
                AnswerHandler cur = new AnswerHandler() {

                    @Override
                    public void onSolution(Sentence belief) {
                        // System.out.println("solution: " + belief);
                        System.out.println(belief);
                        float howconf = belief.truth.getConfidence();
                        if (howconf > 0.1f) {
                            // only mark if above 0.1 confidence
                            // also mark image:
                            int maxu = Integer.valueOf(belief.getTerm().toString().split("example")[1].split("}")[0]);
                            clear();
                            for (int x = 0; x < SZ * scale_palette; x += 1) {
                                for (int y = 0; y < SZ * scale_palette; y += 1) {
                                    Color col = new Color(canvasIMG.getRGB(x / scale_palette, y / scale_palette));
                                    int k = getK[maxu];
                                    int j = getJ[maxu];
                                    exampleIMG.setRGB(x + k * scale_palette * SZ, y + (3 * j + 1) * scale_palette * SZ, new Color(col.getRed(), 0, 0).getRGB());
                                }
                            }
                            estimate.setIcon(new ImageIcon(fitimage(exampleIMG, estimate.getWidth(), estimate.getHeight())));
                            estimate.repaint();
                        }
                    }
                };
                q.add(cur);
                try {
                    for (int h = 0; h < exid; h++) {
                        inputPanel2.setText(inputPanel2.getText() + additional[h] + "\n");
                        nar.addInput(additional[h]);
                    }
                    inputPanel2.setText(inputPanel2.getText() + s + "\n");
                    nar.ask(s.substring(0, s.length() - 1), cur);
                } catch (Narsese.InvalidInputException ex) {
                    Logger.getLogger(SymRecognizer.class.getName()).log(Level.SEVERE, null, ex);
                }
            }
            u++;
        }
        nar.param.noiseLevel.set(0);
        nar.addInput(inputPanel.getText());
        nar.start(0);
    }
}

Example 4 with Sentence

use of nars.entity.Sentence in project opennars by opennars.

the class SymRecognizerWithVisionChannel method jButton3ActionPerformed.

private void jButton3ActionPerformed(java.awt.event.ActionEvent evt) {
    // GEN-FIRST:event_jButton3ActionPerformed
    resetDetection();
    StringBuilder build = new StringBuilder();
    StringBuilder build2 = new StringBuilder();
    build2.append("<(&|,");
    for (int x = 0; x < SZ; x += 1) {
        for (int y = 0; y < SZ; y += 1) {
            int used_X = x;
            int used_Y = y;
            Color col1 = new Color(canvasIMG.getRGB(x, y));
            float col = ((float) col1.getRed()) / 255.0f;
            if (col > 0.0) {
                float freq = 0.5f + (col - 0.5f);
                if (invar.isSelected()) {
                    build.append("<p[" + String.valueOf(used_X) + "," + String.valueOf(used_Y) + "] --> [on]>. :|: %" + String.valueOf(freq) + "%");
                    build.append("\n");
                    build2.append("<p[" + String.valueOf(used_X) + "," + String.valueOf(used_Y) + "] --> [on]>,");
                } else {
                    build.append("<p_" + String.valueOf(used_X) + "_" + String.valueOf(used_Y) + " --> [on]>. :|: %" + String.valueOf(freq) + "%");
                    build.append("\n");
                    build2.append("<p_" + String.valueOf(used_X) + "_" + String.valueOf(used_Y) + " --> [on]>,");
                }
            }
        }
    }
    String s2 = build2.toString();
    s2 = s2.substring(0, s2.length() - 1);
    s2 = s2 + ")";
    inputPanel.setText(build.toString());
    if (evt == null) {
        String question = "<{?what} --> [observed]>?";
        additional[exid] = s2 + " ==> <{example" + exid + "} --> [observed]>>.";
        inputPanel2.setText(additional[exid] + "\n" + question);
    } else {
        // add vision channel for [on] property
        nar = new NAR();
        // to the nar instance, and nar is also the "next higher" sensory channel
        // to report the results to
        nar.addSensoryChannel("[on]", new SpatialSamplingVisionChannel(nar, nar, SZ, SZ));
        if (invar1.isSelected()) {
            gui = new NARSwing(nar);
        }
        int u = 0;
        inputPanel2.setText("");
        // for(String s : questions) {
        String s = question;
        {
            if (s != null) {
                AnswerHandler cur = new AnswerHandler() {

                    @Override
                    public void onSolution(Sentence belief) {
                        // System.out.println("solution: " + belief);
                        System.out.println(belief);
                        float howconf = belief.truth.getConfidence();
                        if (howconf >= 0.001f) {
                            // only mark if above 0.1 confidence
                            // also mark image:
                            int maxu = Integer.valueOf(belief.getTerm().toString().split("example")[1].split("}")[0]);
                            clear();
                            for (int x = 0; x < SZ * scale_palette; x += 1) {
                                for (int y = 0; y < SZ * scale_palette; y += 1) {
                                    Color col = new Color(canvasIMG.getRGB(x / scale_palette, y / scale_palette));
                                    int k = getK[maxu];
                                    int j = getJ[maxu];
                                    exampleIMG.setRGB(x + k * scale_palette * SZ, y + (3 * j + 1) * scale_palette * SZ, new Color(col.getRed(), 0, 0).getRGB());
                                }
                            }
                            estimate.setIcon(new ImageIcon(fitimage(exampleIMG, estimate.getWidth(), estimate.getHeight())));
                            estimate.repaint();
                        }
                    }
                };
                q.add(cur);
                try {
                    for (int h = 0; h < exid; h++) {
                        inputPanel2.setText(inputPanel2.getText() + additional[h] + "\n");
                        nar.addInput(additional[h]);
                    }
                    inputPanel2.setText(inputPanel2.getText() + s + "\n");
                    nar.ask(s.substring(0, s.length() - 1), cur);
                } catch (Narsese.InvalidInputException ex) {
                    Logger.getLogger(SymRecognizerWithVisionChannel.class.getName()).log(Level.SEVERE, null, ex);
                }
            }
            u++;
        }
        nar.param.noiseLevel.set(0);
        nar.addInput(inputPanel.getText());
        nar.start(0);
    }
}

Example 5 with Sentence

use of nars.entity.Sentence in project opennars by opennars.

the class ConceptMonitor method strongestProjectedInputEventBelief.

public static Sentence strongestProjectedInputEventBelief(NAR nar, String st) {
    Concept c = ConceptMonitor.concept(nar, st);
    if (c != null) {
        for (Task t : c.beliefs) {
            if (t.isInput() && !t.sentence.isEternal()) {
                Sentence s = t.sentence;
                Sentence projected = s.projection(nar.memory.time(), nar.memory.time());
                if (!projected.isEternal()) {
                    return projected;
                }
            }
        }
    }
    return null;
}