Examples with LSQROnHeap org.apache.ignite.ml.math.isolve.lsqr.LSQROnHeap used on opensource projects

Example 1 with LSQROnHeap

use of org.apache.ignite.ml.math.isolve.lsqr.LSQROnHeap in project ignite by apache.

the class LinearRegressionLSQRTrainer method fit.

/**
 * {@inheritDoc}
 */
@Override
public LinearRegressionModel fit(DatasetBuilder<K, V> datasetBuilder, IgniteBiFunction<K, V, double[]> featureExtractor, IgniteBiFunction<K, V, Double> lbExtractor, int cols) {
    LSQRResult res;
    try (LSQROnHeap<K, V> lsqr = new LSQROnHeap<>(datasetBuilder, new LinSysPartitionDataBuilderOnHeap<>((k, v) -> {
        double[] row = Arrays.copyOf(featureExtractor.apply(k, v), cols + 1);
        row[cols] = 1.0;
        return row;
    }, lbExtractor, cols + 1))) {
        res = lsqr.solve(0, 1e-12, 1e-12, 1e8, -1, false, null);
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
    Vector weights = new DenseLocalOnHeapVector(Arrays.copyOfRange(res.getX(), 0, cols));
    return new LinearRegressionModel(weights, res.getX()[cols]);
}

Example 2 with LSQROnHeap

use of org.apache.ignite.ml.math.isolve.lsqr.LSQROnHeap in project ignite by apache.

the class LinearRegressionLSQRTrainer method updateModel.

/**
 * {@inheritDoc}
 */
@Override
protected <K, V> LinearRegressionModel updateModel(LinearRegressionModel mdl, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> extractor) {
    LSQRResult res;
    PatchedPreprocessor<K, V, Double, double[]> patchedPreprocessor = new PatchedPreprocessor<>(LinearRegressionLSQRTrainer::extendLabeledVector, extractor);
    try (LSQROnHeap<K, V> lsqr = new LSQROnHeap<>(datasetBuilder, envBuilder, new SimpleLabeledDatasetDataBuilder<>(patchedPreprocessor), learningEnvironment())) {
        double[] x0 = null;
        if (mdl != null) {
            int x0Size = mdl.weights().size() + 1;
            Vector weights = mdl.weights().like(x0Size);
            mdl.weights().nonZeroes().forEach(ith -> weights.set(ith.index(), ith.get()));
            weights.set(weights.size() - 1, mdl.intercept());
            x0 = weights.asArray();
        }
        res = lsqr.solve(0, 1e-12, 1e-12, 1e8, -1, false, x0);
        if (res == null)
            return getLastTrainedModelOrThrowEmptyDatasetException(mdl);
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
    double[] x = res.getX();
    Vector weights = new DenseVector(Arrays.copyOfRange(x, 0, x.length - 1));
    return new LinearRegressionModel(weights, x[x.length - 1]);
}