Examples with SimpleLabeledDatasetDataBuilder org.apache.ignite.ml.dataset.primitive.builder.data.SimpleLabeledDatasetDataBuilder used on opensource projects

Example 1 with SimpleLabeledDatasetDataBuilder

use of org.apache.ignite.ml.dataset.primitive.builder.data.SimpleLabeledDatasetDataBuilder in project ignite by apache.

the class AlgorithmSpecificDatasetExample 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(">>> Algorithm Specific Dataset example started.");
        IgniteCache<Integer, Person> persons = createCache(ignite);
        // labels are extracted, and partition data and context are created.
        try (AlgorithmSpecificDataset dataset = DatasetFactory.create(ignite, persons, (upstream, upstreamSize) -> new AlgorithmSpecificPartitionContext(), new SimpleLabeledDatasetDataBuilder<Integer, Person, AlgorithmSpecificPartitionContext>((k, v) -> new double[] { v.getAge() }, (k, v) -> v.getSalary(), 1).andThen((data, ctx) -> {
            double[] features = data.getFeatures();
            int rows = data.getRows();
            // Makes a copy of features to supplement it by columns with values equal to 1.0.
            double[] a = new double[features.length + rows];
            for (int i = 0; i < rows; i++) a[i] = 1.0;
            System.arraycopy(features, 0, a, rows, features.length);
            return new SimpleLabeledDatasetData(a, rows, data.getCols() + 1, data.getLabels());
        })).wrap(AlgorithmSpecificDataset::new)) {
            // Trains linear regression model using gradient descent.
            double[] linearRegressionMdl = new double[2];
            for (int i = 0; i < 1000; i++) {
                double[] gradient = dataset.gradient(linearRegressionMdl);
                if (BLAS.getInstance().dnrm2(gradient.length, gradient, 1) < 1e-4)
                    break;
                for (int j = 0; j < gradient.length; j++) linearRegressionMdl[j] -= 0.1 / persons.size() * gradient[j];
            }
            System.out.println("Linear Regression Model: " + Arrays.toString(linearRegressionMdl));
        }
        System.out.println(">>> Algorithm Specific Dataset example completed.");
    }
}