Examples with TaskImmediateProcess nars.core.Events.TaskImmediateProcess used on opensource projects

Example 1 with TaskImmediateProcess

use of nars.core.Events.TaskImmediateProcess 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");
            }*/
    }
}