Examples with NAR nars.core.NAR used on opensource projects

Example 1 with NAR

use of nars.core.NAR in project narchy by automenta.

the class ConceptIntegratorExample method main.

public static void main(String[] args) throws Exception {
    int cycles = 500;
    NAR nar = new Default().build();
    NARTrace t = new NARTrace(nar);
    nar.addInput("<a --> b>.");
    nar.addInput("<b --> c>.");
    nar.addInput("<(^pick,x) =\\> a>.");
    nar.addInput("<(*, b, c) <-> x>.");
    nar.addInput("a!");
    ConceptIntegrator ci = new ConceptIntegrator(nar, 16, cycles);
    nar.run(cycles);
    TimelineVis tc = new TimelineVis(ci.getCharts());
    /*
            new BarChart(new FirstOrderDifferenceTimeSeries("d(concepts)", t.charts.get("concept.count"))),

            new StackedPercentageChart(t, "concept.priority.hist.0", "concept.priority.hist.1", "concept.priority.hist.2", "concept.priority.hist.3").height(2),

            new LineChart(
                    new ConceptBagTimeSeries(nar, nar.memory.concepts, cycles, Mode.ConceptPriorityTotal)            
            ).height(4),

            new LineChart(
                    new ConceptBagTimeSeries(nar, nar.memory.concepts, cycles, Mode.TermLinkPriorityMean),
                    new ConceptBagTimeSeries(nar, nar.memory.concepts, cycles, Mode.TaskLinkPriorityMean)
            
            ).height(4),


            new LineChart(t, "task.novel.add", "task.immediate_processed").height(3),
            new LineChart(t, "task.goal.process", "task.question.process", "task.judgment.process").height(3),
            new LineChart(t, "emotion.busy").height(1),
            new EventChart(t, false, false, true).height(3)
        );
        */
    new NWindow("_", new PCanvas(tc)).show(800, 800, true);
}

Example 2 with NAR

use of nars.core.NAR in project narchy by automenta.

the class NARCompare1 method main.

public static void main(String[] args) {
    int cycles = 128;
    String input = "<a --> b>.\n" + "<b --> c>.\n" + "<a --> c>?\n";
    NAR a = new Default().build();
    setA(a.param);
    NARTrace at = new NARTrace(a);
    a.addInput(input);
    a.run(cycles);
    NAR b = new Default().build();
    setB(b.param);
    NARTrace bt = new NARTrace(b);
    b.addInput(input);
    b.run(cycles);
    new NWindow("_", new PCanvas(new TimelineVis(new LineChart(at.getCharts("task.novel.add", "task.immediate_processed")).height(3), new LineChart(at.getCharts("task.goal.process", "task.question.process", "task.judgment.process")).height(3), new LineChart(at.getCharts("concept.priority.mean")).height(2), new LineChart(bt.getCharts("task.novel.add", "task.immediate_processed")).height(3), new LineChart(bt.getCharts("task.goal.process", "task.question.process", "task.judgment.process")).height(3), new LineChart(bt.getCharts("concept.priority.mean")).height(2)))).show(900, 800, true);
}

Example 3 with NAR

use of nars.core.NAR in project narchy by automenta.

the class Predict3 method main.

public static void main(String[] args) throws Narsese.InvalidInputException, InterruptedException {
    Parameters.DEBUG = true;
    int duration = 8;
    float freq = 1.0f / duration * 0.15f;
    int minCyclesAhead = 0;
    double missingDataRate = 0.1;
    double noiseRate = 0.02;
    boolean onlyNoticeChange = false;
    int thinkInterval = onlyNoticeChange ? 1 : 2;
    int discretization = 3;
    NAR n = new NAR(new Default().setInternalExperience(null));
    n.param.duration.set(duration);
    // n.param.duration.setLinear(0.5);
    n.param.conceptBeliefsMax.set(64);
    // n.param.conceptForgetDurations.set(16);
    Discretize discretize = new Discretize(n, discretization);
    TreeMLData observed = new TreeMLData("value", Color.WHITE).setRange(0, 1f);
    TreeMLData[] predictions = new TreeMLData[discretization];
    TreeMLData[] reflections = new TreeMLData[discretization];
    for (int i = 0; i < predictions.length; i++) {
        predictions[i] = new TreeMLData("Pred" + i, Color.getHSBColor(0.25f + i / 4f, 0.85f, 0.85f));
        // predictions[i].setDefaultValue(0.0);
        reflections[i] = new TreeMLData("Refl" + i, Color.getHSBColor(0.25f + i / 4f, 0.85f, 0.85f));
        reflections[i].setDefaultValue(0.0);
    // predictions[i].setRange(0, 0.5);
    }
    TimelineVis tc = new TimelineVis(new LineChart(observed).thickness(16f).height(128), new LineChart(predictions).thickness(16f).height(128), new LineChart(reflections).thickness(16f).height(128));
    // new BarChart(error).height(4)
    new NWindow("_", new PCanvas(tc)).show(800, 800, true);
    new TextOutput(n, System.out) {

        /**
         * dt = relative to center
         */
        public double getPredictionEnvelope(double dt, double duration) {
            // guassian curve width=duration
            // e^(-(4*x/(dur))^2)
            double p = (4 * dt / duration);
            return Math.exp(-(p * p));
        }

        /**
         * only allow future predictions
         */
        protected boolean allowTask(Task t) {
            if (t.sentence.isEternal()) {
                return false;
            }
            boolean future = false;
            if ((t.sentence.getOccurenceTime() > n.time() + minCyclesAhead)) {
                System.out.print(n.time() + ".." + t.sentence.getOccurenceTime() + ": ");
                future = true;
            }
            Term term = t.getTerm();
            int time = (int) t.sentence.getOccurenceTime();
            int value = -1;
            float conf = t.sentence.truth.getConfidence();
            float expect = 2f * (t.sentence.truth.getFrequency() - 0.5f) * conf;
            String ts = term.toString();
            if (ts.startsWith("<x_t0 --> y")) {
                char cc = ts.charAt("<x_t0 --> y".length());
                value = cc - '0';
            }
            if (value != -1) {
                // predictions[(int)value].addPlus(time, expect);
                for (int tt = time - duration / 2; tt <= time + duration / 2; tt++) {
                    double smooth = 1;
                    expect *= getPredictionEnvelope(time - tt, smooth * duration * 2f);
                /*
                            if (future)
                                predictions[value].addPlus(tt, expect);
                            else
                                reflections[value].addPlus(tt, expect);
                            */
                }
            }
            return true;
        }
    };
    for (Term t : discretize.getValueTerms("x")) n.believe(t.toString(), Tense.Present, 0.5f, 0.5f);
    // TODO move this to discretize.getDisjunctionBelief
    n.believe("<(||,y0,y1,y2,y3,y4,y5,y6,y7) --> y>", Tense.Eternal, 1.0f, 0.95f);
    n.run(discretization * 4);
    // new TextOutput(n, System.out);
    Concept[] valueBeliefConcepts = discretize.getValueConcepts("x");
    NARSwing.themeInvert();
    new NWindow("x", new ConceptsPanel(n, valueBeliefConcepts)).show(900, 600, true);
    RNNBeliefPrediction predictor = new RNNBeliefPrediction(n, valueBeliefConcepts) {

        @Override
        public double[] getTrainedPrediction(double[] input) {
            return input;
        }

        @Override
        public int getPredictionSize() {
            return getInputSize();
        // return 1;
        }

        @Override
        protected double[] predict() {
            double[] x = super.predict();
            if (x == null)
                return null;
            long t = n.time();
            for (int i = 0; i < x.length; i++) {
                // - x[i*2+1]);
                predictions[i].add((int) t, x[i]);
            }
            return x;
        }
    };
    // new NARSwing(n);
    int prevY = -1, curY = -1;
    long prevT = n.time();
    while (true) {
        n.run(thinkInterval);
        Thread.sleep(3);
        // n.memory.addSimulationTime(1);
        signal = (float) Math.max(0, Math.min(1.0, Math.tan(freq * n.time()) * 0.5f + 0.5f));
        // signal  = (float)Math.sin(freq * n.time()) * 0.5f + 0.5f;
        // signal = ((float) Math.sin(freq * n.time()) > 0 ? 1f : -1f) * 0.5f + 0.5f;
        signal *= 1.0 + (Math.random() - 0.5f) * 2f * noiseRate;
        if (Math.random() > missingDataRate)
            observed.add((int) n.time(), signal);
        prevY = curY;
        curY = discretize.i(signal);
        if ((curY == prevY) && (onlyNoticeChange)) {
            continue;
        }
        discretize.believe("x", signal, 0);
        // input(prevT, Term.get("x_t0"), prevY, Term.get("x_t0"), curY, 1f, n.memory);
        // input(prevT, Term.get("x_t0"), prevY, Term.get("x_t0"), 1f-curY, 0.5f, n.memory);
        // n.addInput("notice(x_t0," + value + ",(||,y0,y1))!");
        // input(prevT, Term.get("x_tMin1"), prevY, Term.get("x_t0"), 1f-y, 0f, n.memory);
        // input(Term.get("x_t0"), Term.get(value), 0.0f, 0.0f, n.memory);              //input(Term.get("x_t0"), Term.get(otherValue), 1.0f, 0f, n.memory);  */
        /*
            n.believe(xFuncEq0, Tense.Present, 1.0f, y);
            n.believe(xFuncEq0, Tense.Present, 0.0f, 1f - y);
            n.believe(xFuncEq1, Tense.Present, 1.0f, 1f - y);
            n.believe(xFuncEq1, Tense.Present, 0.0f, y);
                    */
        prevT = n.time();
    }
}

Example 4 with NAR

use of nars.core.NAR in project narchy by automenta.

the class Predict_NARS_Core method main.

public static void main(String[] args) throws Narsese.InvalidInputException, InterruptedException {
    Parameters.DEBUG = true;
    int duration = 8;
    float freq = 1.0f / duration * 0.1f;
    int thinkInterval = 50;
    double discretization = 3;
    NAR n = new NAR(new Default());
    n.param.duration.set(duration);
    n.param.noiseLevel.set(0);
    // n.param.conceptForgetDurations.set(16);
    n.on(TaskImmediateProcess.class, new TaskImmediateProcess() {

        int curmax = 0;

        @Override
        public void onProcessed(Task t, NAL n) {
            if (t.sentence.getOccurenceTime() >= n.memory.time() && t.sentence.truth.getExpectation() > 0.5) {
                Term term = t.getTerm();
                int time = (int) t.sentence.getOccurenceTime();
                int value = -1;
                String ts = term.toString();
                if (ts.startsWith("<{x} --> y")) {
                    char cc = ts.charAt("<{x} --> y".length());
                    value = cc - '0';
                    if (time >= curmax) {
                        curmax = time;
                    }
                    maxval = Math.max(maxval, (value) / 10.0);
                    predictions[0].add(time, (value) / 10.0);
                }
            }
        }
    });
    TreeMLData observed = new TreeMLData("value", Color.WHITE).setRange(0, 1f);
    predictions = new TreeMLData[(int) discretization];
    TreeMLData[] reflections = new TreeMLData[(int) discretization];
    for (int i = 0; i < predictions.length; i++) {
        predictions[i] = new TreeMLData("Pred" + i, Color.getHSBColor(0.25f + i / 4f, 0.85f, 0.85f));
        reflections[i] = new TreeMLData("Refl" + i, Color.getHSBColor(0.25f + i / 4f, 0.85f, 0.85f));
        reflections[i].setDefaultValue(0.0);
    }
    TimelineVis tc = new TimelineVis(new LineChart(0, 1, observed).thickness(16f).height(128), new LineChart(predictions[0]).thickness(16f).height(128));
    new NWindow("_", new PCanvas(tc)).show(800, 800, true);
    n.run((int) discretization * 4);
    NARSwing.themeInvert();
    new NARSwing(n);
    ChangedTextInput chg = new ChangedTextInput(n);
    double lastsignal = 0;
    double lasttime = 0;
    while (true) {
        n.run(thinkInterval);
        Thread.sleep(30);
        // signal  = (float)Math.max(0, Math.min(1.0, Math.tan(freq * n.time()) * 0.5f + 0.5f));
        signal = (float) Math.sin(freq * n.time()) * 0.5f + 0.5f;
        // signal = ((float) Math.sin(freq * n.time()) > 0 ? 1f : -1f) * 0.5f + 0.5f;
        // signal *= 1.0 + (Math.random()-0.5f)* 2f * noiseRate;
        // this
        observed.removeData((int) (lasttime + 1));
        // is not good practice
        observed.removeData((int) (lasttime + 2));
        observed.add((int) n.time(), signal);
        // but is fine
        observed.add((int) n.time() + 1, -1);
        // for now (just wanted a line at the end)
        observed.add((int) n.time() + 2, 1);
        lastsignal = signal;
        lasttime = n.time();
        predictions[0].setData(0, maxval);
        // if(cnt<1000) { //switch to see what NARS does when observations end :)
        int val = (int) (((int) ((signal * discretization)) * (10.0 / discretization)));
        chg.set("<{x} --> y" + val + ">. :|:");
    // System.out.println(val);
    /*} else if (cnt==1000){
                System.out.println("observation phase end, residual predictions follow");
            }*/
    }
}

Example 5 with NAR

use of nars.core.NAR in project narchy by automenta.

the class RLNAR method main.

public static void main(String[] args) {
    NAR n = new Default().build();
    AgentLoader L = new AgentLoader(new RLNAR(n));
    L.run();
}