Examples with DatasetBuilder org.apache.ignite.ml.dataset.DatasetBuilder used on opensource projects

Example 1 with DatasetBuilder

use of org.apache.ignite.ml.dataset.DatasetBuilder 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 DatasetBuilder

use of org.apache.ignite.ml.dataset.DatasetBuilder in project ignite by apache.

the class NormalizationExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws Exception {
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Normalization example started.");
        IgniteCache<Integer, Person> persons = createCache(ignite);
        DatasetBuilder<Integer, Person> builder = new CacheBasedDatasetBuilder<>(ignite, persons);
        // Defines first preprocessor that extracts features from an upstream data.
        IgniteBiFunction<Integer, Person, double[]> featureExtractor = (k, v) -> new double[] { v.getAge(), v.getSalary() };
        // Defines second preprocessor that normalizes features.
        NormalizationPreprocessor<Integer, Person> preprocessor = new NormalizationTrainer<Integer, Person>().fit(builder, featureExtractor, 2);
        // Creates a cache based simple dataset containing features and providing standard dataset API.
        try (SimpleDataset<?> dataset = DatasetFactory.createSimpleDataset(builder, preprocessor, 2)) {
            // Calculation of the mean value. This calculation will be performed in map-reduce manner.
            double[] mean = dataset.mean();
            System.out.println("Mean \n\t" + Arrays.toString(mean));
            // Calculation of the standard deviation. This calculation will be performed in map-reduce manner.
            double[] std = dataset.std();
            System.out.println("Standard deviation \n\t" + Arrays.toString(std));
            // Calculation of the covariance matrix.  This calculation will be performed in map-reduce manner.
            double[][] cov = dataset.cov();
            System.out.println("Covariance matrix ");
            for (double[] row : cov) System.out.println("\t" + Arrays.toString(row));
            // Calculation of the correlation matrix.  This calculation will be performed in map-reduce manner.
            double[][] corr = dataset.corr();
            System.out.println("Correlation matrix ");
            for (double[] row : corr) System.out.println("\t" + Arrays.toString(row));
        }
        System.out.println(">>> Normalization example completed.");
    }
}