Examples with LabeledVector org.apache.ignite.ml.structures.LabeledVector used on opensource projects

Example 11 with LabeledVector

use of org.apache.ignite.ml.structures.LabeledVector in project ignite by apache.

the class MnistDistributed method asLabeledVector.

/**
 * Transform vector created by {@link MnistUtils} to {@link LabeledVector}.
 *
 * @param v Vector to transform.
 * @param featsCnt Count of features.
 * @return Vector created by {@link MnistUtils} transformed to {@link LabeledVector}.
 */
private static LabeledVector<Vector, Vector> asLabeledVector(Vector v, int featsCnt) {
    Vector features = VectorUtils.copyPart(v, 0, featsCnt);
    Vector lb = VectorUtils.num2Vec((int) v.get(featsCnt), 10);
    return new LabeledVector<>(features, lb);
}

Example 12 with LabeledVector

use of org.apache.ignite.ml.structures.LabeledVector in project ignite by apache.

the class MnistDistributed method testMNISTDistributed.

/**
 */
public void testMNISTDistributed() throws IOException {
    int samplesCnt = 60_000;
    int hiddenNeuronsCnt = 100;
    IgniteBiTuple<Stream<DenseLocalOnHeapVector>, Stream<DenseLocalOnHeapVector>> trainingAndTest = loadMnist(samplesCnt);
    // Load training mnist part into a cache.
    Stream<DenseLocalOnHeapVector> trainingMnist = trainingAndTest.get1();
    List<DenseLocalOnHeapVector> trainingMnistLst = trainingMnist.collect(Collectors.toList());
    IgniteCache<Integer, LabeledVector<Vector, Vector>> labeledVectorsCache = LabeledVectorsCache.createNew(ignite);
    loadIntoCache(trainingMnistLst, labeledVectorsCache);
    MLPGroupUpdateTrainer<RPropParameterUpdate> trainer = MLPGroupUpdateTrainer.getDefault(ignite).withMaxGlobalSteps(35).withSyncPeriod(2);
    MLPArchitecture arch = new MLPArchitecture(FEATURES_CNT).withAddedLayer(hiddenNeuronsCnt, true, Activators.SIGMOID).withAddedLayer(10, false, Activators.SIGMOID);
    MultilayerPerceptron mdl = trainer.train(new MLPGroupUpdateTrainerCacheInput(arch, 9, labeledVectorsCache, 2000));
    IgniteBiTuple<Matrix, Matrix> testDs = createDataset(trainingAndTest.get2(), 10_000, FEATURES_CNT);
    Vector truth = testDs.get2().foldColumns(VectorUtils::vec2Num);
    Vector predicted = mdl.apply(testDs.get1()).foldColumns(VectorUtils::vec2Num);
    Tracer.showAscii(truth);
    Tracer.showAscii(predicted);
    X.println("Accuracy: " + VectorUtils.zipWith(predicted, truth, (x, y) -> x.equals(y) ? 1.0 : 0.0).sum() / truth.size() * 100 + "%.");
}

Example 13 with LabeledVector

use of org.apache.ignite.ml.structures.LabeledVector in project ignite by apache.

the class MLPGroupUpdateTrainerCacheInput method batchSupplier.

/**
 * {@inheritDoc}
 */
@Override
public IgniteSupplier<IgniteBiTuple<Matrix, Matrix>> batchSupplier() {
    String cName = cache.getName();
    // This line is for prohibiting of 'this' object be caught into serialization context of lambda.
    int bs = batchSize;
    // This line is for prohibiting of 'this' object be caught into serialization context of lambda.
    Random r = rand;
    return () -> {
        Ignite ignite = Ignition.localIgnite();
        IgniteCache<Integer, LabeledVector<Vector, Vector>> cache = ignite.getOrCreateCache(cName);
        int total = cache.size();
        Affinity<Integer> affinity = ignite.affinity(cName);
        List<Integer> allKeys = IntStream.range(0, total).boxed().collect(Collectors.toList());
        List<Integer> keys = new ArrayList<>(affinity.mapKeysToNodes(allKeys).get(ignite.cluster().localNode()));
        int locKeysCnt = keys.size();
        int[] selected = Utils.selectKDistinct(locKeysCnt, Math.min(bs, locKeysCnt), r);
        // Get dimensions of vectors in cache. We suppose that every feature vector has
        // same dimension d 1 and every label has the same dimension d2.
        LabeledVector<Vector, Vector> dimEntry = cache.get(keys.get(selected[0]));
        Matrix inputs = new DenseLocalOnHeapMatrix(dimEntry.features().size(), bs);
        Matrix groundTruth = new DenseLocalOnHeapMatrix(dimEntry.label().size(), bs);
        for (int i = 0; i < selected.length; i++) {
            LabeledVector<Vector, Vector> labeled = cache.get(keys.get(selected[i]));
            inputs.assignColumn(i, labeled.features());
            groundTruth.assignColumn(i, labeled.label());
        }
        return new IgniteBiTuple<>(inputs, groundTruth);
    };
}

Example 14 with LabeledVector

use of org.apache.ignite.ml.structures.LabeledVector in project ignite by apache.

the class KNNModel method classify.

/**
 */
private double classify(LabeledVector[] neighbors, Vector v, KNNStrategy stgy) {
    Map<Double, Double> clsVotes = new HashMap<>();
    for (int i = 0; i < neighbors.length; i++) {
        LabeledVector neighbor = neighbors[i];
        double clsLb = (double) neighbor.label();
        double distance = cachedDistances != null ? cachedDistances[i] : distanceMeasure.compute(v, neighbor.features());
        if (clsVotes.containsKey(clsLb)) {
            double clsVote = clsVotes.get(clsLb);
            clsVote += getClassVoteForVector(stgy, distance);
            clsVotes.put(clsLb, clsVote);
        } else {
            final double val = getClassVoteForVector(stgy, distance);
            clsVotes.put(clsLb, val);
        }
    }
    return getClassWithMaxVotes(clsVotes);
}