Examples with Vector org.apache.ignite.ml.math.Vector used on opensource projects

Example 96 with Vector

use of org.apache.ignite.ml.math.Vector in project ignite by apache.

the class AbstractVector method logNormalize.

/**
 * @param power Power.
 * @param normLen Normalized length.
 * @return logNormalized value.
 */
private Vector logNormalize(double power, double normLen) {
    assert !(Double.isInfinite(power) || power <= 1.0);
    double denominator = normLen * Math.log(power);
    Vector cp = copy();
    for (Element element : cp.all()) element.set(Math.log1p(element.get()) / denominator);
    return cp;
}

Example 97 with Vector

use of org.apache.ignite.ml.math.Vector in project ignite by apache.

the class AbstractVector method plus.

/**
 * {@inheritDoc}
 */
@Override
public Vector plus(Vector vec) {
    checkCardinality(vec);
    Vector cp = copy();
    return cp.map(vec, Functions.PLUS);
}

Example 98 with Vector

use of org.apache.ignite.ml.math.Vector 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 99 with Vector

use of org.apache.ignite.ml.math.Vector in project ignite by apache.

the class GradientDescent method calculateDistributedGradient.

/**
 * Calculates gradient based in distributed matrix using {@link SparseDistributedMatrixMapReducer}.
 *
 * @param data Distributed matrix
 * @param weights Point to calculate gradient
 * @return Gradient
 */
private Vector calculateDistributedGradient(SparseDistributedMatrix data, Vector weights) {
    SparseDistributedMatrixMapReducer mapReducer = new SparseDistributedMatrixMapReducer(data);
    return mapReducer.mapReduce((matrix, args) -> {
        Matrix inputs = extractInputs(matrix);
        Vector groundTruth = extractGroundTruth(matrix);
        return lossGradient.compute(inputs, groundTruth, args);
    }, gradients -> {
        int cnt = 0;
        Vector resGradient = new DenseLocalOnHeapVector(data.columnSize());
        for (Vector gradient : gradients) {
            if (gradient != null) {
                resGradient = resGradient.plus(gradient);
                cnt++;
            }
        }
        return resGradient.divide(cnt);
    }, weights);
}

Example 100 with Vector

use of org.apache.ignite.ml.math.Vector in project ignite by apache.

the class QRDSolver method solve.

/**
 * Least squares solution of {@code A*X = B}; {@code returns X}.
 *
 * @param mtx A matrix with as many rows as {@code A} and any number of cols.
 * @return {@code X<} that minimizes the two norm of {@code Q*R*X - B}.
 * @throws IllegalArgumentException if {@code B.rows() != A.rows()}.
 */
public Matrix solve(Matrix mtx) {
    if (mtx.rowSize() != q.rowSize())
        throw new IllegalArgumentException("Matrix row dimensions must agree.");
    int cols = mtx.columnSize();
    Matrix x = like(r, r.columnSize(), cols);
    Matrix qt = q.transpose();
    Matrix y = qt.times(mtx);
    for (int k = Math.min(r.columnSize(), q.rowSize()) - 1; k >= 0; k--) {
        // X[k,] = Y[k,] / R[k,k], note that X[k,] starts with 0 so += is same as =
        x.viewRow(k).map(y.viewRow(k), Functions.plusMult(1 / r.get(k, k)));
        if (k == 0)
            continue;
        // Y[0:(k-1),] -= R[0:(k-1),k] * X[k,]
        Vector rCol = r.viewColumn(k).viewPart(0, k);
        for (int c = 0; c < cols; c++) y.viewColumn(c).viewPart(0, k).map(rCol, Functions.plusMult(-x.get(k, c)));
    }
    return x;
}