use of spacegraph.space2d.container.Gridding in project narchy by automenta.
the class ExeCharts method runPanel.
public static Surface runPanel(NAR n) {
Label nameLabel;
LoopPanel control = new LoopPanel(n.loop);
Surface p = new Gridding(nameLabel = new Label(n.self().toString()), control);
return DurSurface.get(p, n, () -> {
control.update();
});
}
use of spacegraph.space2d.container.Gridding in project narchy by automenta.
the class Line1DCalibrate method conceptPlot.
public static Gridding conceptPlot(NAR nar, Iterable<FloatSupplier> concepts, int plotHistory) {
// TODO make a lambda Grid constructor
Gridding grid = new Gridding(VERTICAL);
List<Plot2D> plots = $.newArrayList();
for (FloatSupplier t : concepts) {
Plot2D p = new Plot2D(plotHistory, Plot2D.Line);
p.add(t.toString(), t::asFloat, 0f, 1f);
grid.add(p);
plots.add(p);
}
grid.layout();
nar.onCycle(f -> {
plots.forEach(Plot2D::update);
});
return grid;
}
use of spacegraph.space2d.container.Gridding 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;
}
use of spacegraph.space2d.container.Gridding in project narchy by automenta.
the class PhyWall method snake.
protected Snake snake(Wire wire, Runnable onRemove) {
Surface source = wire.a;
Surface target = wire.b;
assert (source != target);
float sa = source.bounds.area();
float ta = target.bounds.area();
float areaDiff = Math.abs(sa - ta) / (sa + ta);
// heuristic estimate: larger area difference = shorter snake
int segments = Util.lerp(areaDiff, 8, 6);
float EXPAND_SCALE_FACTOR = 4;
PushButton deleteButton = new PushButton("x");
Surface menu = new TabPane(ButtonSet.Mode.Multi, Map.of("o", () -> new Gridding(new Label(source.toString()), new Label(target.toString()), deleteButton)), (l) -> new CheckBox(l) {
@Override
protected String label(String text, boolean on) {
// override just display the 'o'
return text;
}
@Override
public ToggleButton set(boolean expanded) {
super.set(expanded);
synchronized (wire) {
PhyWindow w = parent(PhyWindow.class);
if (w == null)
return this;
float cx = w.cx();
float cy = w.cy();
float ww, hh;
if (expanded) {
// grow
ww = w.w() * EXPAND_SCALE_FACTOR;
hh = w.h() * EXPAND_SCALE_FACTOR;
} else {
// shrink
ww = w.w() / EXPAND_SCALE_FACTOR;
hh = w.h() / EXPAND_SCALE_FACTOR;
}
w.pos(cx - ww / 2, cy - hh / 2, cx + ww / 2, cy + hh / 2);
}
return this;
}
});
PhyWindow menuBody = put(menu, RectFloat2D.mid(source.bounds, target.bounds, 0.1f));
float mw = menuBody.radius();
Snake s = new Snake(source, target, segments, 1.618f * 2 * mw, mw) {
@Override
public void remove() {
onRemove.run();
super.remove();
}
};
s.attach(menuBody.body, segments / 2 - 1);
deleteButton.click(s::remove);
int jj = 0;
for (Joint j : s.joints) {
float p = ((float) jj) / (segments - 1);
// custom joint renderer: color coded indicate activity and type of data
j.setData((ObjectLongProcedure<GL2>) (g, now) -> {
int TIME_DECAY_MS = 250;
boolean side = p < 0.5f;
float activity = wire.activity(side, now, TIME_DECAY_MS);
// Util.lerp(p, wire.activity(false, now, TIME_DECAY_MS), wire.activity(true, now, TIME_DECAY_MS));
int th = wire.typeHash(side);
if (th == 0) {
g.glColor4f(0.5f, 0.5f, 0.5f, 0.5f);
} else {
Draw.colorHash(g, th, 0.9f, 0.5f + 0.5f * activity, 0.5f + 0.4f * activity);
}
g.glLineWidth(10f + activity * 10f);
// Draw.line(g, w.a.cx(), w.a.cy(), w.b.cx(), w.b.cy());
// return;
});
jj++;
}
return s;
}
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