Examples with NARSwing nars.gui.NARSwing used on opensource projects

Example 1 with NARSwing

use of nars.gui.NARSwing 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 2 with NARSwing

use of nars.gui.NARSwing 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 3 with NARSwing

use of nars.gui.NARSwing in project opennars by opennars.

the class KeyboardInputExample method main.

public static void main(String[] args) {
    // NAR n = NAR.build(new Neuromorphic().realTime());
    // NAR n = NAR.build(new Default().realTime());
    // n.param.duration.set(100);
    NARSwing.themeInvert();
    NAR n = new NAR();
    new NARSwing(n).themeInvert();
    new NWindow("Direct Keyboard Input", new KeyboardInputPanel(n)).show(300, 100, false);
    n.start(100);
}

Example 4 with NARSwing

use of nars.gui.NARSwing 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 NARSwing

use of nars.gui.NARSwing 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;
    }
}