Examples with DenseVector org.apache.ignite.ml.math.primitives.vector.impl.DenseVector used on opensource projects

Example 21 with DenseVector

use of org.apache.ignite.ml.math.primitives.vector.impl.DenseVector in project ignite by apache.

the class BlasTest method testGemvSparseSparseDense.

/**
 * Tests 'gemv' operation for sparse matrix A, sparse vector x and dense vector y.
 */
@Test
public void testGemvSparseSparseDense() {
    // y := alpha * A * x + beta * y
    double alpha = 3.0;
    DenseMatrix a = new DenseMatrix(new double[][] { { 10.0, 11.0 }, { 0.0, 1.0 } }, 2);
    SparseVector x = sparseFromArray(new double[] { 1.0, 2.0 });
    double beta = 2.0;
    DenseVector y = new DenseVector(new double[] { 3.0, 4.0 });
    DenseVector exp = (DenseVector) y.times(beta).plus(a.times(x).times(alpha));
    Blas.gemv(alpha, a, x, beta, y);
    Assert.assertEquals(exp, y);
}

Example 22 with DenseVector

use of org.apache.ignite.ml.math.primitives.vector.impl.DenseVector in project ignite by apache.

the class BlasTest method testSyrNonSquareMatrix.

/**
 * Tests 'syr' operation for non-square dense matrix A.
 */
@Test(expected = NonSquareMatrixException.class)
public void testSyrNonSquareMatrix() {
    double alpha = 3.0;
    DenseMatrix a = new DenseMatrix(new double[][] { { 10.0, 11.0, 12.0 }, { 0.0, 1.0, 2.0 } }, 2);
    Vector x = new DenseVector(new double[] { 1.0, 2.0 });
    new Blas().syr(alpha, x, a);
}

Example 23 with DenseVector

use of org.apache.ignite.ml.math.primitives.vector.impl.DenseVector in project ignite by apache.

the class BlasTest method testGemvDenseDenseDense.

/**
 * Tests 'gemv' operation for dense matrix A, dense vector x and dense vector y.
 */
@Test
public void testGemvDenseDenseDense() {
    // y := alpha * A * x + beta * y
    double alpha = 3.0;
    DenseMatrix a = new DenseMatrix(new double[][] { { 10.0, 11.0 }, { 0.0, 1.0 } }, 2);
    DenseVector x = new DenseVector(new double[] { 1.0, 2.0 });
    double beta = 2.0;
    DenseVector y = new DenseVector(new double[] { 3.0, 4.0 });
    DenseVector exp = (DenseVector) y.times(beta).plus(a.times(x).times(alpha));
    Blas.gemv(alpha, a, x, beta, y);
    Assert.assertEquals(exp, y);
}

Example 24 with DenseVector

use of org.apache.ignite.ml.math.primitives.vector.impl.DenseVector in project ignite by apache.

the class LinearRegressionSGDTrainer method updateModel.

/**
 * {@inheritDoc}
 */
@Override
protected <K, V> LinearRegressionModel updateModel(LinearRegressionModel mdl, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> extractor) {
    assert updatesStgy != null;
    IgniteFunction<Dataset<EmptyContext, SimpleLabeledDatasetData>, MLPArchitecture> archSupplier = dataset -> {
        int cols = dataset.compute(data -> {
            if (data.getFeatures() == null)
                return null;
            return data.getFeatures().length / data.getRows();
        }, (a, b) -> {
            if (a == null)
                return b == null ? 0 : b;
            if (b == null)
                return a;
            return b;
        });
        MLPArchitecture architecture = new MLPArchitecture(cols);
        architecture = architecture.withAddedLayer(1, true, Activators.LINEAR);
        return architecture;
    };
    MLPTrainer<?> trainer = new MLPTrainer<>(archSupplier, LossFunctions.MSE, updatesStgy, maxIterations, batchSize, locIterations, seed);
    IgniteFunction<LabeledVector<Double>, LabeledVector<double[]>> func = lv -> new LabeledVector<>(lv.features(), new double[] { lv.label() });
    PatchedPreprocessor<K, V, Double, double[]> patchedPreprocessor = new PatchedPreprocessor<>(func, extractor);
    MultilayerPerceptron mlp = Optional.ofNullable(mdl).map(this::restoreMLPState).map(m -> trainer.update(m, datasetBuilder, patchedPreprocessor)).orElseGet(() -> trainer.fit(datasetBuilder, patchedPreprocessor));
    double[] p = mlp.parameters().getStorage().data();
    return new LinearRegressionModel(new DenseVector(Arrays.copyOf(p, p.length - 1)), p[p.length - 1]);
}

Example 25 with DenseVector

use of org.apache.ignite.ml.math.primitives.vector.impl.DenseVector in project ignite by apache.

the class Deltas method getStateVector.

/**
 * @param mdl Model.
 * @return vector of model weights with intercept.
 */
private Vector getStateVector(SVMLinearClassificationModel mdl) {
    double intercept = mdl.intercept();
    Vector weights = mdl.weights();
    int stateVectorSize = weights.size() + 1;
    Vector res = weights.isDense() ? new DenseVector(stateVectorSize) : new SparseVector(stateVectorSize);
    res.set(0, intercept);
    weights.nonZeroes().forEach(ith -> res.set(ith.index(), ith.get()));
    return res;
}