Examples with TreeMLData automenta.vivisect.TreeMLData used on opensource projects

Example 6 with TreeMLData

use of automenta.vivisect.TreeMLData in project opennars by opennars.

the class LineChart method getStart.

@Override
public int getStart() {
    start = Integer.MAX_VALUE;
    end = 0;
    for (TreeMLData s : data) {
        int ss = s.getStart();
        int se = s.getEnd();
        if (start > ss)
            start = ss;
        if (end < se)
            end = se;
    }
    return start;
}

Example 7 with TreeMLData

use of automenta.vivisect.TreeMLData in project opennars by opennars.

the class SpectrumChart method update.

protected void update() {
    TreeMLData chart = data.get(0);
    float length = chart.getEnd() - chart.getStart();
    ;
    int numWindows = (int) Math.ceil(length / windowSize);
    // TODO dont remove existing windows
    windows.clear();
    int t = 0;
    for (int w = 0; w < numWindows; w++) {
        final int vl = windowSize;
        double[] input = new double[vl];
        for (int i = 0; i < vl; i++) {
            input[i] = (float) chart.getData(t++);
        }
        Complex[] c = fft.transform(input, TransformType.FORWARD);
        // phase = atan2( imaginary , real )
        // magnitude = sqrt( real<sup>2</sup> + imaginary<sup>2</sup> )
        windows.add(new Window(c));
    }
}

Example 8 with TreeMLData

use of automenta.vivisect.TreeMLData in project opennars by opennars.

the class StackedPercentageChart method drawData.

@Override
protected void drawData(TimelineVis l, float timeScale, float yScale, float y) {
    l.g.noStroke();
    for (int t = l.cycleStart; t < l.cycleEnd; t++) {
        float total = 0;
        for (TreeMLData chart : data) {
            float v = (float) chart.getData(t);
            if (Float.isNaN(v)) {
                continue;
            }
            total += v;
        }
        if (total == 0) {
            continue;
        }
        float sy = y;
        float gap = yScale * (1.0f - barHeight) / data.size();
        l.g.strokeWeight(1f);
        for (TreeMLData chart : data) {
            int ccolor = chart.getColor();
            float x = (t - l.cycleStart) * timeScale;
            float v = (float) chart.getData(t);
            if (Float.isNaN(v)) {
                continue;
            }
            float p = v / total;
            float px = width * x;
            float h = p * yScale;
            l.g.fill(ccolor, 255f * (0.5f + 0.5f * p));
            l.g.rect(px, sy + gap / 2, width * timeScale * barWidth, h - gap / 2);
            sy += h;
        }
    }
}

Example 9 with TreeMLData

use of automenta.vivisect.TreeMLData 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;
    }
}