Examples with BELIEF nars.Op.BELIEF used on opensource projects

Example 1 with BELIEF

use of nars.Op.BELIEF in project narchy by automenta.

the class RTreeBeliefTableTest method testAccuracy.

static void testAccuracy(int dur, int period, int end, int cap, LongToFloatFunction func) {
    NAR n = NARS.shell();
    n.time.dur(dur);
    Term term = $.p("x");
    // 1. populate
    // n.log();
    TaskConcept c = (TaskConcept) n.conceptualize(term);
    @NotNull BeliefTable cb = true ? c.beliefs() : c.goals();
    cb.setCapacity(0, cap);
    // int numTasks = 0;
    System.out.println("points:");
    long time;
    long start = n.time();
    while ((time = n.time()) < end) {
        float f = func.valueOf(time);
        System.out.print(time + "=" + f + "\t");
        n.input($.task(term, BELIEF, f, 0.9f).time(time).setPriThen(0.5f).apply(n));
        n.run(period);
        c.beliefs().print();
        System.out.println();
    // numTasks++;
    }
    System.out.println();
    System.out.println();
    MultiStatistics<Task> m = new MultiStatistics<Task>().classify("input", (t) -> t.isInput()).classify("derived", (t) -> t instanceof DerivedTask).value("pri", (t) -> t.pri()).value2D("truth", (t) -> new float[] { t.freq(), t.conf() }).value("freqErr", (t) -> Math.abs(((t.freq() - 0.5f) * 2f) - func.valueOf(t.mid()))).add(c.beliefs().streamTasks().collect(toList()));
    System.out.println();
    m.print();
    System.out.println();
    c.beliefs().print();
    // 2. validate and calculate error
    CSVOutput csv = new CSVOutput(System.out, "time", "actual", "approx");
    double errSum = 0;
    for (long i = start; i < end; i++) {
        float actual = func.valueOf(i);
        Truth actualTruth = n.beliefTruth(term, i);
        float approx, err;
        if (actualTruth != null) {
            approx = actualTruth.freq();
            err = Math.abs(approx - actual);
        } else {
            approx = Float.NaN;
            err = 1f;
        }
        errSum += err;
        csv.out(i, actual, approx);
    // System.out.println(n2(i) + "\t" + /*n2(err) + "\t" + */ n2(expected) + "\t" + n2(actual));
    }
    double avgErr = errSum / (end - start + 1);
    System.out.println();
    System.out.println(n4(avgErr) + " avg freq err per point");
    assertTrue(avgErr < 0.1f);
}

Example 2 with BELIEF

use of nars.Op.BELIEF in project narchy by automenta.

the class NAct method actionBipolarFrequencyDifferential.

default GoalActionAsyncConcept[] actionBipolarFrequencyDifferential(@NotNull Term s, boolean fair, boolean latchPreviousIfUndecided, @NotNull FloatToFloatFunction update) {
    Term pt = // $.p(s, PLUS);
    $.inh(s, PLUS);
    // $.prop(s,PLUS);
    // $.p(s, ZeroProduct);
    // $.p(s,$.the("\"+\""));
    Term nt = // $.p(s, NEG);
    $.inh(s, NEG);
    // $.prop(s, NEG);
    // $.p(ZeroProduct, s);
    // $.p(s,$.the("\"-\""));
    final float[] g = new float[2];
    final float[] c = new float[2];
    final long[] lastUpdate = { ETERNAL };
    final float[] lastX = { 0 };
    // hack
    GoalActionAsyncConcept[] CC = new GoalActionAsyncConcept[2];
    @NotNull BiConsumer<GoalActionAsyncConcept, Truth> u = (action, gg) -> {
        NAR n = nar();
        long now = n.time();
        if (now != lastUpdate[0]) {
            lastUpdate[0] = now;
            // reset
            CC[0] = CC[1] = null;
        }
        // float freqEps = n.freqResolution.floatValue();
        float confMin = n.confMin.floatValue();
        // float eviMin = c2wSafe(confMin);
        float feedbackConf = // fairly shared to sum to default
        w2c(c2w(n.confDefault(BELIEF)) / 2f);
        // n.confDefault(BELIEF);
        // n.confDefault(GOAL);
        // confMin * ...;
        boolean p = action.term().equals(pt);
        int ip = p ? 0 : 1;
        CC[ip] = action;
        g[ip] = gg != null ? // gg.freq()
        gg.expectation() : 0f;
        // 0.5f;
        c[ip] = gg != null ? // gg.evi()
        gg.conf() : 0f;
        // -1..+1
        float x;
        boolean curious;
        if (CC[0] != null && CC[1] != null) /* both ready */
        {
            float cMax = Math.max(c[0], c[1]);
            float cMin = Math.min(c[0], c[1]);
            float coherence = cMin / cMax;
            Random rng = n.random();
            float cur = curiosity().floatValue();
            if (cur > 0 && rng.nextFloat() <= cur) {
                x = (rng.nextFloat() - 0.5f) * 2f;
                // float curiEvi =
                // //c2w(n.confDefault(BELIEF));
                // //eviMin*2;
                // Math.max(c2wSafe(w2cSafe(eviMin)*2), Util.mean(c[0], c[1])); //match desire conf, min=2*minConf
                c[0] = c[1] = feedbackConf;
                coherence = 1f;
                curious = true;
            } else {
                curious = false;
                if (cMax < confMin) {
                    if (latchPreviousIfUndecided) {
                        x = lastX[0];
                    } else {
                        x = 0;
                    }
                } else {
                    // //expectation
                    // float g0 = g[0]-0.5f;
                    // float g1 = g[1]-0.5f;
                    // df = 2f * ((g0) - (g1));
                    // // /Math.max(Math.abs(g0), Math.abs(g1));
                    // frequency -======================
                    // A. subtraction
                    // subtract
                    x = ((g[0] - g[1]));
                    // experimental: lessen by a factor of how equally confident each goal is
                    if (fair) {
                        // fully fair
                        x *= coherence;
                    // x *= Math.sqrt(coherence); //less sharp than linear
                    // semi-fair
                    // df *= 0.5f + 0.5f * (eMin / eMax); //reduction by at most half
                    }
                // df *= 1f - Math.abs(e[0] - e[1]) / eMax;
                // df *= Util.sqr(eMin / eMax); //more cautious
                // df *= Math.min(w2cSafe(e[0]), w2cSafe(e[1])) / w2cSafe(eMax);
                }
            }
            x = Util.clamp(x, -1f, +1f);
            lastX[0] = x;
            // -1..+1
            float y = update.valueOf(x);
            // System.out.println(x + " " + y);
            // w2c(Math.abs(y) * c2w(restConf));
            PreciseTruth Nb, Ng, Pb, Pg;
            if (y == y) {
                // y: (-1..+1)
                float yp, yn;
                if (Math.abs(y) >= n.freqResolution.floatValue()) {
                    yp = 0.5f + y / 2f;
                    yn = 1f - yp;
                } else {
                    yp = yn = 0.5f;
                }
                // float yp = 0.5f + y/2f;
                // float yn = 1f - yp;
                float pbf = yp;
                float nbf = yn;
                Pb = $.t(pbf, feedbackConf);
                Nb = $.t(nbf, feedbackConf);
                // float goalEvi =
                // eviMin;
                // //max(eviMin, max(e[0], e[1]));
                // Pg = curious || e[0] == 0 ? new PreciseTruth(yp, goalEvi, false) : null;
                // Ng = curious || e[1] == 0 ? new PreciseTruth(yn, goalEvi, false) : null;
                // float confBase = confMin*4; //~ alpha, learning rate
                // float fThresh = Float.MIN_NORMAL;
                // float yp = y > +fThresh ? Util.lerp(+y, confBase, feedbackConf) : confBase;
                // float yn = y < -fThresh ? Util.lerp(-y, confBase, feedbackConf) : confBase;
                // Pb = $.t(y > +fThresh ? 1 : 0, y > +fThresh ? yp : feedbackConf - yp);
                // Nb = $.t(y < -fThresh ? 1 : 0, y < -fThresh ? yn : feedbackConf - yn);
                // //Pg = curious || e[0] == 0 ? new PreciseTruth(1, Util.lerp(+y, confMin2, feedbackConf)) : null;
                // Pg = null;
                // //Ng = curious || e[1] == 0 ? new PreciseTruth(1, Util.lerp(-y, confMin2, feedbackConf)) : null;
                // Ng = null;
                // float fThresh = nar().freqResolution.floatValue();
                // int sign = (y > fThresh ? +1 : (y < -fThresh ? -1 : 0));
                // 
                // float feedConf =
                // w2cSafe(c2wSafe(goalConf)/2f); //half/half
                // //goalConf;
                // //Math.max(confMin, goalConf * coherence);
                // switch (sign) {
                // case +1:
                // //Pb = $.t(1f, Util.lerp(+y, confBase, feedbackConf));
                // Pb = $.t(y/2f + 0.5f, feedConf);
                // Nb =
                // //null;
                // $.t(0, feedConf);
                // break;
                // case -1:
                // Pb =
                // //null;
                // $.t(0, feedConf);
                // 
                // Nb = $.t(-y/2f + 0.5f, feedConf);
                // break;
                // case 0:
                // //Pb = Nb = null; //no signal
                // Pb = Nb = $.t(0, feedConf);
                // //Math.max(confMin, feedConf);
                // //w2cSafe(c2wSafe(feedConf)/2f))); //zero
                // break;
                // default:
                // throw new UnsupportedOperationException();
                // }
                Pg = null;
                Ng = null;
            // if (curious) {
            // e[0] = e[1] = 0; //reset to get full evidence override
            // }
            // float g0 = eviMax - e[0];
            // Pg = g0 >= eviMin ? new PreciseTruth(yp, g0, false) : null;
            // float g1 = eviMax - e[1];
            // Ng = g1 >= eviMin ? new PreciseTruth(yn, g1, false) : null;
            } else {
                Pb = Nb = Pg = Ng = null;
            }
            // System.out.println(Pb + "," + Nb + " <- " + g[0] + ";" + c[0] + ", " + g[1] + ';' + c[1]);
            CC[0].feedback(Pb, Pg, n);
            CC[1].feedback(Nb, Ng, n);
        }
    };
    CauseChannel<ITask> cause = nar().newCauseChannel(s);
    GoalActionAsyncConcept p = new GoalActionAsyncConcept(pt, this, cause, u);
    GoalActionAsyncConcept n = new GoalActionAsyncConcept(nt, this, cause, u);
    addAction(p);
    addAction(n);
    nar().believe($.inh(s, SECTe.the(PLUS, NEG)).neg(), Tense.Eternal);
    CC[0] = p;
    CC[1] = n;
    return CC;
}

Example 3 with BELIEF

use of nars.Op.BELIEF in project narchy by automenta.

the class NAgentX method runRT.

public static NAR runRT(Function<NAR, NAgent> init, float narFPS, float agentFPS) {
    // The.Subterms.the =
    // The.Subterms.CaffeineSubtermBuilder.get();
    // The.Subterms.HijackSubtermBuilder.get();
    // The.Subterms.SoftSubtermBuilder.get();
    // The.Compound.the =
    // The.Compound.
    // //SoftCompoundBuilder.get();
    // CaffeineCompoundBuilder.get();
    float clockFPS = // agentFPS;
    narFPS;
    RealTime clock = clockFPS >= 10 / 2f ? /* nyquist threshold between decisecond (0.1) and centisecond (0.01) clock resolution */
    new RealTime.CS(true) : new RealTime.DSHalf(true);
    clock.durFPS(clockFPS);
    // Function<NAR, PrediTerm<Derivation>> deriver = Deriver.deriver(8
    // , "motivation.nal"
    // //., "relation_introduction.nal"
    // );
    // int THREADS = Math.max(1, Runtime.getRuntime().availableProcessors() - 1);
    // Predicate<Activate> randomBool = (a) -> ThreadLocalRandom.current().nextBoolean();
    // exe.add(new FocusExec(), (x) -> true);
    // exe.add(new FocusExec() {
    // {
    // concepts.setCapacity(32);
    // }
    // },
    // (x) -> true);
    NAR n = new NARS().exe(new WorkerMultiExec(// new Focus.DefaultRevaluator(),
    new Focus.AERevaluator(new XoRoShiRo128PlusRandom(1)), 256, 8192) {

        {
            Util.setExecutor(this);
        }
    }).time(clock).deriverAdd(1, 1).deriverAdd(2, 2).deriverAdd(3, 3).deriverAdd(5, 5).deriverAdd(6, 8).deriverAdd("motivation.nal").index(new CaffeineIndex(// 800 * 1024,
    2500 * 1024, // Integer.MAX_VALUE,
    c -> {
        return (int) Math.ceil(c.voluplexity());
    // return Math.round(
    // ((float)c.voluplexity())
    // / (1 + 100 * (c.termlinks().priSum() + c.tasklinks().priSum()))
    // //(c.beliefs().size() + c.goals().size()))
    // );
    })).get();
    // n.defaultWants();
    n.dtMergeOrChoose.set(true);
    // 0.5f //nyquist
    n.dtDither.set(1f);
    // n.timeFocus.set(4);
    n.confMin.set(0.01f);
    n.freqResolution.set(0.01f);
    n.termVolumeMax.set(40);
    n.beliefConfDefault.set(0.9f);
    n.goalConfDefault.set(0.9f);
    float priFactor = 0.2f;
    n.beliefPriDefault.set(1f * priFactor);
    n.goalPriDefault.set(1f * priFactor);
    n.questionPriDefault.set(1f * priFactor);
    n.questPriDefault.set(1f * priFactor);
    n.activationRate.set(0.5f);
    NAgent a = init.apply(n);
    // new RLBooster(a, HaiQAgent::new, 1);
    // //            @Override
    // //            protected long matchTime(Task task) {
    // //
    // //                //future lookahead to catalyze prediction
    // //                return n.time() +
    // //                        Util.sqr(n.random().nextInt(3)) * n.dur();
    // //
    // //            }
    // };
    // {
    // AgentBuilder b = MetaGoal.newController(a);
    // //                .in(a::dexterity)
    // //                .in(new FloatNormalized(()->a.reward).decay(0.9f))
    // //                .in(new FloatNormalized(
    // //                        ((Emotivation) n.emotion).cycleDTRealMean::getValue)
    // //                            .decay(0.9f)
    // //                )
    // b.in(new FloatNormalized(
    // //TODO use a Long-specific impl of this:
    // new FloatFirstOrderDifference(n::time, () -> n.emotion.deriveTask.getValue().longValue())
    // ).relax(0.99f))
    // //                .in(new FloatNormalized(
    // //                        //TODO use a Long-specific impl of this:
    // //                        new FirstOrderDifferenceFloat(n::time, () -> n.emotion.conceptFirePremises.getValue().longValue())
    // //                    ).decay(0.9f)
    // .in(new FloatNormalized(
    // () -> n.emotion.busyVol.getSum()
    // ).relax(0.99f))
    // .out(2, (onOff)->{
    // switch(onOff) {
    // case 0:
    // a.enabled.set(false); //pause
    // break;
    // case 1:
    // a.enabled.set(true); //un-pause
    // break;
    // }
    // })
    // //                ).out(
    // //                        new StepController((x) -> n.time.dur(Math.round(x)), 1, n.dur(), n.dur()*2)
    // //                .out(
    // //                        StepController.harmonic(n.confMin::set, 0.01f, 0.5f)
    // //                )//.out(
    // //                        StepController.harmonic(n.truthResolution::setValue, 0.01f, 0.08f)
    // //                ).out(
    // //                        StepController.harmonic(a.curiosity::setValue, 0.01f, 0.16f)
    // //                ).get(n);
    // 
    // ;
    // new AgentService(new MutableFloat(1), n, b.get());
    // }
    // n.dtMergeOrChoose.setValue(true);
    // STMLinkage stmLink = new STMLinkage(n, 1, false);
    // LinkClustering linkClusterPri = new LinkClustering(n, Prioritized::priElseZero /* anything temporal */,
    // 32, 128);
    // LinkClustering linkClusterConf = new LinkClustering(n, (t) -> t.isBeliefOrGoal() ? t.conf() : Float.NaN,
    // 4, 16);
    // SpaceGraph.window(col(
    // new STMView.BagClusterVis(n, linkClusterPri.bag),
    // new STMView.BagClusterVis(n, linkClusterConf.bag)
    // ), 800, 600);
    // ConjClustering conjClusterBinput = new ConjClustering(n, BELIEF, (Task::isInput), 8, 32);
    ConjClustering conjClusterBany = new ConjClustering(n, BELIEF, (t -> true), 8, 64);
    // ConjClustering conjClusterG = new ConjClustering(n, GOAL, (t -> true), 4, 16);
    // ArithmeticIntroduction arith = new ArithmeticIntroduction(4, n);
    // RelationClustering relCluster = new RelationClustering(n,
    // (t)->t.isBelief() && !t.isEternal() && !t.term().isTemporal() ? t.conf() : Float.NaN,
    // 8, 32);
    // ConjClustering conjClusterG = new ConjClustering(n, GOAL, (t->true),8, 32);
    // n.runLater(() -> {
    // //            AudioContext ac = new AudioContext();
    // //            ac.start();
    // //            Clock aclock = new Clock(ac, 1000f / (agentFPS * 0.5f));
    // //            new Metronome(aclock, n);
    // new VocalCommentary(null, a);
    // //ac.out.dependsOn(aclock);
    // });
    // /needs tryContent before its safe
    Inperience inp = new Inperience(n, 12);
    // 
    // Abbreviation abb = new Abbreviation(n, "z", 3, 6, 10f, 32);
    // reflect.ReflectSimilarToTaskTerm refSim = new reflect.ReflectSimilarToTaskTerm(16, n);
    // reflect.ReflectClonedTask refTask = new reflect.ReflectClonedTask(16, n);
    // a.trace = true;
    // n.onTask(t -> {
    // if (t instanceof DerivedTask)
    // System.out.println(t);
    // });
    // NInner nin = new NInner(n);
    // nin.start();
    // AgentService mc = MetaGoal.newController(a);
    // init();
    // n.onCycle(nn -> {
    // float lag = narLoop.lagSumThenClear() + a.running().lagSumThenClear();
    // //n.emotion.happy(-lag);
    // //n.emotion.happy(n.emotion.busyPri.getSum()/50000f);
    // });
    // new Anoncepts(8, n);
    // new Implier(2f, a,
    // 1
    // //0,1,4
    // );
    // 
    // window(new MatrixView(p.in, (x, gl) -> {
    // Draw.colorBipolar(gl, x);
    // return 0;
    // }), 100, 100);
    // get ready
    System.gc();
    n.runLater(() -> {
        chart(a);
        SpaceGraph.window(Vis.top(a.nar()), 800, 800);
        // window(new ConceptView(a.happy,n), 800, 600);
        n.on(a);
        // START AGENT
        Loop aLoop = a.runFPS(agentFPS);
    // n.runLater(() -> {
    // new Deriver(a.fire(), Derivers.deriver(6, 8,
    // "motivation.nal"
    // //, "goal_analogy.nal"
    // ).apply(n).deriver, n); //{
    // });
    });
    Loop loop = n.startFPS(narFPS);
    return n;
}

Example 4 with BELIEF

use of nars.Op.BELIEF in project narchy by automenta.

the class Recog2D method conceptTraining.

Surface conceptTraining(BeliefVector tv, NAR nar) {
    // LinkedHashMap<TaskConcept, BeliefVector.Neuron> out = tv.out;
    Plot2D p;
    int history = 256;
    Gridding g = new Gridding(p = new Plot2D(history, Plot2D.Line).add("Reward", () -> reward), new AspectAlign(new CameraSensorView(sp, this), AspectAlign.Align.Center, sp.width, sp.height), new Gridding(beliefTableCharts(nar, List.of(tv.concepts), 16)), new Gridding(IntStream.range(0, tv.concepts.length).mapToObj(i -> new spacegraph.space2d.widget.text.Label(String.valueOf(i)) {

        @Override
        protected void paintBelow(GL2 gl) {
            Concept c = tv.concepts[i];
            BeliefVector.Neuron nn = tv.neurons[i];
            float freq, conf;
            Truth t = nar.beliefTruth(c, nar.time());
            if (t != null) {
                conf = t.conf();
                freq = t.freq();
            } else {
                conf = nar.confMin.floatValue();
                float defaultFreq = // interpret no-belief as maybe
                0.5f;
                // Float.NaN  //use NaN to force learning of negation as separate from no-belief
                freq = defaultFreq;
            }
            Draw.colorBipolar(gl, 2f * (freq - 0.5f));
            float m = 0.5f * conf;
            Draw.rect(gl, bounds);
            if (tv.verify) {
                float error = nn.error;
                if (error != error) {
                // training phase
                // Draw.rect(gl, m / 2, m / 2, 1 - m, 1 - m);
                } else {
                // verification
                // draw backgroudn/border
                // gl.glColor3f(error, 1f - error, 0f);
                // 
                // float fontSize = 0.08f;
                // gl.glColor3f(1f, 1f, 1f);
                // Draw.text(gl, c.term().toString(), fontSize, m / 2, 1f - m / 2, 0);
                // Draw.text(gl, "err=" + n2(error), fontSize, m / 2, m / 2, 0);
                }
            }
        }
    }).toArray(Surface[]::new)));
    final int[] frames = { 0 };
    onFrame(() -> {
        if (frames[0]++ % imagePeriod == 0) {
            nextImage();
        }
        redraw();
        // if (neural.get()) {
        // if (nar.time() < trainFrames) {
        outs.expect(image);
        if (neural.get()) {
            train.update(mlpLearn, mlpSupport);
        }
        p.update();
    // s.update();
    });
    return g;
}

Example 5 with BELIEF

use of nars.Op.BELIEF in project narchy by automenta.

the class ThermostatTest method main.

// @Test
// @Disabled
public static void main(String[] args) {
    // void test1() {
    // Param.DEBUG = true;
    final int DUR = 1;
    final int subTrainings = 2;
    // pause between episodes
    final int thinkDurs = 4;
    NAR n = NARS.tmp();
    n.time.dur(DUR);
    n.timeFocus.set(2);
    n.termVolumeMax.set(34);
    // n.freqResolution.set(0.05f);
    // n.confResolution.set(0.01f);
    n.activationRate.set(0.5f);
    n.goalPriDefault.set(1f);
    // n.forgetRate.set(2f);
    // n.deep.set(0.8);
    // n.emotion.want(MetaGoal.Desire, 0.2f);
    // n.want(MetaGoal.Believe, 0.1f);
    // n.want(MetaGoal.Perceive, -0.01f);
    float exeThresh = 0.51f;
    // new ArithmeticIntroduction(8, n);
    new ConjClustering(n, BELIEF, (t) -> true, 8, 32);
    // n.priDefault(BELIEF, 0.3f);
    // n.logPriMin(System.out, 0.5f);
    // n.logWhen(System.out, false, true, true);
    // n.log();
    boolean[] training = new boolean[] { true };
    Opjects op = new Opjects(n) {

        // {
        // pretend = true;
        // }
        @Override
        @Nullable
        protected synchronized Object invoked(Object obj, Method wrapped, Object[] args, Object result) {
            if (training[0]) {
                n.synch();
            // n.runLater(nn -> nn.run(DUR)); //queue some thinking cycles
            }
            Object y = super.invoked(obj, wrapped, args, result);
            if (training[0])
                n.run(DUR * thinkDurs);
            return y;
        }
    };
    Teacher<Thermostat> env = new Teacher<>(op, Thermostat.class);
    Consumer<Thermostat> hotToCold = Thermostat.change(true, false), coldToCold = Thermostat.change(false, false), coldToHot = Thermostat.change(false, true), hotToHot = Thermostat.change(true, true);
    Predicate<Thermostat> isCold = x -> x.is() == Thermostat.cold;
    Predicate<Thermostat> isHot = x -> x.is() == Thermostat.hot;
    n.logWhen(System.out, true, true, true);
    boolean stupid = true;
    training: do {
        training[0] = true;
        op.exeThresh.set(1f);
        for (int i = 0; i < subTrainings; i++) {
            for (Consumer<Thermostat> condition : new Consumer[] { hotToCold, coldToCold }) {
                System.out.println("EPISODE START");
                n.clear();
                env.teach("down", condition, (Thermostat x) -> {
                    // x.up(); //demonstrate no change
                    // x.report();
                    n.run(1);
                    while (x.is() > Thermostat.cold) {
                        x.down();
                        n.run(1);
                    }
                    x.report();
                    n.run(1);
                // x.down(); //demonstrate no change
                // x.report();
                }, isCold);
                System.out.println("EPISODE END");
                n.run(thinkDurs * n.dur());
            // n.concept("do(down)").print();
            }
            for (Consumer<Thermostat> condition : new Consumer[] { coldToHot, hotToHot }) {
                System.out.println("EPISODE START");
                n.clear();
                env.teach("up", condition, x -> {
                    // x.down(); //demonstrate no change
                    // x.report();
                    n.run(1);
                    while (!isHot.test(x)) {
                        x.up();
                        n.run(1);
                    }
                    x.report();
                    n.run(1);
                // x.up(); //demonstrate no change
                // x.report();
                }, isHot);
                System.out.println("EPISODE END");
                n.run(thinkDurs * n.dur());
            }
        }
        System.out.println("VALIDATING");
        System.out.println();
        training[0] = false;
        op.exeThresh.set(exeThresh);
        // n.log();
        // n.run(100);
        // new Implier(n, new float[] { 1f },
        // $.$("a_Thermostat(down,())"),
        // $.$("a_Thermostat(up,())")
        // //$.$("a_Thermostat(is,(),#x)")
        // );
        // try {
        // make cold
        // n.input(new NALTask($.$("a_Thermostat(should,(),0)"),
        // BELIEF, $.t(1f, 0.99f),
        // n.time(), n.time(), n.time()+1000,
        // n.time.nextInputStamp()).pri(1f));
        Thermostat t = env.x;
        {
            // n.clear();
            t.is(3);
            t.should(0);
            n.run(thinkDurs * n.dur());
            Term cold = $.$$("is(a_Thermostat,0)");
            // Term cold = $.$safe("(a_Thermostat(is,(),0) &| --a_Thermostat(is,(),3))");
            Term hot = $.$$("is(a_Thermostat,3)");
            Truth goalTruth = $.t(1f, 0.9f);
            DurService xPos = n.wantWhile(cold, goalTruth, new TaskConceptLogger(n, (w) -> (t.current != t.target)));
            DurService xNeg = n.wantWhile(hot, goalTruth.neg(), new TaskConceptLogger(n, (w) -> t.current != t.target));
            n.run(1);
            for (int i = 0; i < 16 && xPos.isOn(); i++) {
                int period = 100;
                // t.report();
                // n.run(period, pause);
                n.run(period);
            }
            xPos.off();
            xNeg.off();
            t.report();
            if (t.is() == t.should()) {
                System.out.println("good job nars!");
                n.believe($.$$("(learn(up) && learn(down))"), Tense.Present);
                stupid = false;
            } else {
                System.out.println("bad job nars! try again");
                n.believe($.$$("(--learn(up) && --learn(down))"), Tense.Present);
            }
        // n.input(new NALTask($.$safe("a_Thermostat(is,(),0)"),
        // GOAL, $.t(1f, 0.95f),
        // n.time(), n.time(), n.time() + periods,
        // n.time.nextInputStamp()).pri(1f));
        // n.input(new NALTask($.$safe("a_Thermostat(is,(),3)"),
        // GOAL, $.t(0f, 0.95f),
        // n.time(), n.time(), n.time() + periods,
        // n.time.nextInputStamp()).pri(1f));
        }
    } while (false);
    // n.run(thinkDurs * n.dur());
    {
    // n.input(new NALTask($.$safe("a_Thermostat(is,(),3)"),
    // GOAL, $.t(0f, 0.99f),
    // n.time(), n.time(), n.time()+1000,
    // n.time.nextInputStamp()).pri(1f));
    }
    // while (t.is() != t.should()) {
    // int period = 1000;
    // t.report();
    // n.run(period);
    // }
    n.tasks().forEach(t -> {
        if (!t.isInput())
            System.out.println(t);
    });
}