Examples with Tensor jcog.math.tensor.Tensor used on opensource projects

Example 1 with Tensor

use of jcog.math.tensor.Tensor in project narchy by automenta.

the class TensorGlow method main.

public static void main(String[] args) {
    PhyWall p = SpaceGraph.wall(1200, 1000);
    p.W.setGravity(new v2(0, -2.8f));
    staticBox(p.W, -5, -8, +5, 2f, false, true, true, true);
    for (int j = 0; j < 3; j++) {
        BodyDef bodyDef2 = new BodyDef();
        bodyDef2.type = BodyType.DYNAMIC;
        // otocenie
        bodyDef2.angle = -0.6f;
        // smer pohybu
        bodyDef2.linearVelocity = new v2(0.0f, 0.0f);
        // rotacia (rychlost rotacie)
        bodyDef2.angularVelocity = 0.0f;
        Body2D newBody = p.W.addBody(bodyDef2);
        PolygonShape shape2 = new PolygonShape();
        shape2.setAsBox(0.25f, 0.25f);
        Fixture f = newBody.addFixture(shape2, 1.0f);
        // trenie
        f.friction = 0.5f;
        // odrazivost
        f.restitution = 0.0f;
        f.material = new Uniform();
        f.material.m_rigidity = 1.0f;
    }
    // //ceiling rack
    // addBox(p.W, -1, +0.4f, 0, +0.65f, false, true, true, true);
    // new Pacman(p.W);
    {
        p.W.setContactListener(new Explosives.ExplosionContacts());
        TheoJansen t = new TheoJansen(p.W, 0.35f);
        PhyWall.PhyWindow pw = p.put(new Gridding(0.5f, new Port((float[] v) -> {
            // System.out.println(v);
            t.motorJoint.setMotorSpeed(v[0] * 2 - v[1] * 2);
            t.motorJoint.setMaxMotorTorque(v[2]);
            t.motorJoint.enableLimit(true);
            t.motorJoint.setLimits((float) (-v[3] * Math.PI), (float) (+v[4] * Math.PI));
            if (v[5] > 0.5f) {
                t.gun.fire();
            }
            t.turretJoint.setLimits((float) (+Math.PI / 2 + v[6] * Math.PI - 0.1f), (float) (+Math.PI / 2 + v[6] * Math.PI + 0.1f));
        })), 0.8f, 0.4f);
        p.W.addJoint(new RevoluteJoint(p.W, new RevoluteJointDef(pw.body, t.chassis)));
    }
    {
        p.W.setParticleRadius(0.05f);
        p.W.setParticleDamping(0.1f);
        CircleShape shape = new CircleShape();
        shape.center.set(0, 10);
        shape.radius = 2f;
        ParticleGroupDef pd = new ParticleGroupDef();
        pd.flags = ParticleType.b2_waterParticle;
        // b2_viscousParticle;
        // b2_elasticParticle;
        // b2_springParticle;
        // b2_powderParticle;
        pd.color = new ParticleColor(0.7f, 0.1f, 0.1f, 0.8f);
        pd.shape = shape;
        p.W.addParticles(pd);
    }
    HaiQae q = new HaiQae(8, 2);
    float[] in = new float[q.ae.inputs()];
    final Tensor randomVector = Tensor.randomVectorGauss(in.length, 0, 1, rng);
    final FloatRange lerpRate = new FloatRange(0.01f, 0, 1f);
    final TensorLERP lerpVector = new TensorLERP(randomVector, lerpRate);
    PhyWall.PhyWindow w = p.put(new Gridding(0.25f, new AutoUpdateMatrixView(lerpVector.data), new LabeledPane("lerp", new XYSlider().on((x, y) -> {
        lerpRate.set(x);
    })), new LabeledPane("out", new Port((x) -> {
    }) {

        @Override
        public void prePaint(int dtMS) {
            super.prePaint(dtMS);
            out(lerpVector.data);
        }
    })), 0.5f, 0.5f);
    p.put(new TogglePort(), 0.25f, 0.25f);
    PhyWall.PhyWindow qw = p.put(new Gridding(new Label("HaiQ"), new AutoSurface<>(q), new LabeledPane("input", new Port((float[] i) -> {
        System.arraycopy(i, 0, in, 0, i.length);
    })), new Gridding(VERTICAL, new AutoUpdateMatrixView(in), new AutoUpdateMatrixView(q.ae.x), new AutoUpdateMatrixView(q.ae.W), new AutoUpdateMatrixView(q.ae.y)), new Gridding(VERTICAL, new AutoUpdateMatrixView(q.q), new AutoUpdateMatrixView(q.et))), 1, 1);
    Loop.of(() -> {
        lerpVector.update();
        q.act((((float) Math.random()) - 0.5f) * 2, in);
    }).runFPS(25);
}

Example 2 with Tensor

use of jcog.math.tensor.Tensor in project narchy by automenta.

the class BagTest method testBagSamplingDistribution.

static void testBagSamplingDistribution(Bag<PLink<String>, PLink<String>> bag, float batchSizeProp) {
    // bag.forEach(System.out::println);
    int cap = bag.capacity();
    int batchSize = (int) Math.ceil(batchSizeProp * cap);
    int batches = cap * 1000 / batchSize;
    Tensor f1 = samplingPriDist(bag, batches, batchSize, Math.min(10, Math.max(2, cap / 2)));
    String h = "cap=" + cap + " total=" + (batches * batchSize);
    System.out.println(h + ":\n\t" + f1.tsv2());
    System.out.println();
    float[] ff = f1.get();
    // monotonically increasing
    // TODO minimize this
    float orderThresh = 0.1f;
    for (int j = 0; j < ff.length; j++) {
        for (int i = j + 1; i < ff.length; i++) {
            float diff = ff[j] - ff[i];
            boolean unordered = diff > orderThresh;
            if (unordered) {
                fail("sampling distribution not ordered. contents=" + bag.toString());
            }
        }
    }
    // should be higher
    final float MIN_RATIO = 1.5f;
    for (int lows : ff.length > 4 ? new int[] { 0, 1 } : new int[] { 0 }) {
        for (int highs : ff.length > 4 ? new int[] { ff.length - 1, ff.length - 2 } : new int[] { ff.length - 1 }) {
            float maxMinRatio = ff[highs] / ff[lows];
            assertTrue(maxMinRatio > MIN_RATIO, maxMinRatio + " ratio between max and min");
        }
    }
// TODO verify the histogram resulting from the above execution is relatively flat:
// ex: [0.21649484536082475, 0.2268041237113402, 0.28865979381443296, 0.26804123711340205]
// the tests below assume that it begins with a relatively flat distribution
// System.out.println(Arrays.toString(bag.priHistogram(4)));
// System.out.println(Arrays.toString(bag.priHistogram(8)));
// System.out.print("Sampling: " );
// printDist(samplingPriDistribution((CurveBag) n.core.concepts, 1000));
// System.out.print("Priority: " );
// EmpiricalDistribution pri;
// printDist(pri = getSamplingPriorityDistribution(n.core.concepts, 1000));
// 
// List<SummaryStatistics> l = pri.getBinStats();
// assertTrue(l.get(0).getN() < l.get(l.size() - 1).getN());
}

Example 3 with Tensor

use of jcog.math.tensor.Tensor in project narchy by automenta.

the class TensorTest method test1DTensorChain.

@Test
public void test1DTensorChain() {
    ArrayTensor a = new ArrayTensor(4);
    a.set(1, 2);
    ArrayTensor b = new ArrayTensor(2);
    b.set(2, 0);
    Tensor ab = TensorChain.get(a, b);
    assertEquals(1, ab.shape().length);
    assertEquals(6, ab.shape()[0]);
    final String[] s = { "" };
    ab.forEach((i, v) -> s[0] += v + " ");
    assertEquals("[0.0 0.0 1.0 0.0 2.0 0.0 ]", Arrays.toString(s));
}

Example 4 with Tensor

use of jcog.math.tensor.Tensor in project narchy by automenta.

the class AgentBuilder method get.

public WiredAgent get() {
    final int inputs = sensors.stream().mapToInt(Tensor::volume).sum();
    final int outputs = actions.stream().mapToInt(IntObjectPair::getOne).sum() + (NOP_ACTION ? 1 : 0);
    Consumer<float[]> inputter = (f) -> {
        int s = sensors.size();
        int j = 0;
        for (int i = 0; i < s; i++) {
            Tensor x = sensors.get(i);
            x.writeTo(f, j);
            j += x.volume();
        }
        assert (j == f.length);
    };
    IntConsumer act = (c) -> {
        int s = actions.size();
        for (int i = 0; i < s; i++) {
            IntObjectPair<? extends IntConsumer> aa = actions.get(i);
            int bb = aa.getOne();
            if (c >= bb) {
                c -= bb;
            } else {
                aa.getTwo().accept(c);
                return;
            }
        }
    };
    return new WiredAgent(a, inputs, inputter, reward, outputs, act);
}