Package org.apache.commons.math3.ml.neuralnet

Examples of org.apache.commons.math3.ml.neuralnet.Neuron$SerializationProxy

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                return Double.compare(weightedResidual(o1),
                                      weightedResidual(o2));
            }

            private double weightedResidual(final PointVectorValuePair pv) {
                final RealVector v = new ArrayRealVector(pv.getValueRef(), false);
                final RealVector r = target.subtract(v);
                return r.dotProduct(weight.operate(r));
            }
        };
    }
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                default:
                    throw new MathInternalError(); // Cannot happen.
                }

                final Neuron aNeuron = network.getNeuron(identifiers[i][j]);
                for (long b : linkEnd) {
                    final Neuron bNeuron = network.getNeuron(b);
                    // Link to all neighbours.
                    // The reverse links will be added as the loop proceeds.
                    network.addLink(aNeuron, bNeuron);
                }
            }
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        final ByteArrayInputStream bis = new ByteArrayInputStream(bos.toByteArray());
        final ObjectInputStream ois = new ObjectInputStream(bis);
        final NeuronSquareMesh2D in = (NeuronSquareMesh2D) ois.readObject();

        for (Neuron nOut : out.getNetwork()) {
            final Neuron nIn = in.getNetwork().getNeuron(nOut.getIdentifier());

            // Same values.
            final double[] outF = nOut.getFeatures();
            final double[] inF = nIn.getFeatures();
            Assert.assertEquals(outF.length, inF.length);
            for (int i = 0; i < outF.length; i++) {
                Assert.assertEquals(outF[i], inF[i], 0d);
            }
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    private List<Neuron> getNeuronList() {
        // Sequence of coordinates.
        final List<Neuron> list = new ArrayList<Neuron>();

        // First neuron.
        Neuron current = net.getNeuron(FIRST_NEURON_ID);
        while (true) {
            list.add(current);
            final Collection<Neuron> neighbours
                = net.getNeighbours(current, list);
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        final ByteArrayInputStream bis = new ByteArrayInputStream(bos.toByteArray());
        final ObjectInputStream ois = new ObjectInputStream(bis);
        final NeuronString in = (NeuronString) ois.readObject();

        for (Neuron nOut : out.getNetwork()) {
            final Neuron nIn = in.getNetwork().getNeuron(nOut.getIdentifier());

            // Same values.
            final double[] outF = nOut.getFeatures();
            final double[] inF = nIn.getFeatures();
            Assert.assertEquals(outF.length, inF.length);
            for (int i = 0; i < outF.length; i++) {
                Assert.assertEquals(outF[i], inF[i], 0d);
            }
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        final double[] distancesBefore = new double[netSize];
        int count = 0;
        for (Neuron n : net) {
            distancesBefore[count++] = dist.compute(n.getFeatures(), features);
        }
        final Neuron bestBefore = MapUtils.findBest(features, net, dist);

        // Initial distance from the best match is larger than zero.
        Assert.assertTrue(dist.compute(bestBefore.getFeatures(), features) >= 0.2);

        update.update(net, features);

        final double[] distancesAfter = new double[netSize];
        count = 0;
        for (Neuron n : net) {
            distancesAfter[count++] = dist.compute(n.getFeatures(), features);
        }
        final Neuron bestAfter = MapUtils.findBest(features, net, dist);

        Assert.assertEquals(bestBefore, bestAfter);
        // Distance is now zero.
        Assert.assertEquals(0, dist.compute(bestAfter.getFeatures(), features), 0d);

        for (int i = 0; i < netSize; i++) {
            // All distances have decreased.
            Assert.assertTrue(distancesAfter[i] < distancesBefore[i]);
        }
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        final ByteArrayInputStream bis = new ByteArrayInputStream(bos.toByteArray());
        final ObjectInputStream ois = new ObjectInputStream(bis);
        final NeuronString in = (NeuronString) ois.readObject();

        for (Neuron nOut : out.getNetwork()) {
            final Neuron nIn = in.getNetwork().getNeuron(nOut.getIdentifier());

            // Same values.
            final double[] outF = nOut.getFeatures();
            final double[] inF = nIn.getFeatures();
            Assert.assertEquals(outF.length, inF.length);
            for (int i = 0; i < outF.length; i++) {
                Assert.assertEquals(outF[i], inF[i], 0d);
            }
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        final ByteArrayInputStream bis = new ByteArrayInputStream(bos.toByteArray());
        final ObjectInputStream ois = new ObjectInputStream(bis);
        final NeuronSquareMesh2D in = (NeuronSquareMesh2D) ois.readObject();

        for (Neuron nOut : out.getNetwork()) {
            final Neuron nIn = in.getNetwork().getNeuron(nOut.getIdentifier());

            // Same values.
            final double[] outF = nOut.getFeatures();
            final double[] inF = nIn.getFeatures();
            Assert.assertEquals(outF.length, inF.length);
            for (int i = 0; i < outF.length; i++) {
                Assert.assertEquals(outF[i], inF[i], 0d);
            }
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    private List<Neuron> getNeuronList() {
        // Sequence of coordinates.
        final List<Neuron> list = new ArrayList<Neuron>();

        // First neuron.
        Neuron current = net.getNeuron(FIRST_NEURON_ID);
        while (true) {
            list.add(current);
            final Collection<Neuron> neighbours
                = net.getNeighbours(current, list);
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     */
    public void update(Network net,
                       double[] features) {
        final long numCalls = numberOfCalls.incrementAndGet();
        final double currentLearning = learningFactor.value(numCalls);
        final Neuron best = findAndUpdateBestNeuron(net,
                                                    features,
                                                    currentLearning);

        final int currentNeighbourhood = neighbourhoodSize.value(numCalls);
        // The farther away the neighbour is from the winning neuron, the
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Related Classes of org.apache.commons.math3.ml.neuralnet.Neuron$SerializationProxy

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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