Examples with LinearRegressionLSQRTrainer org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer used on opensource projects

Example 1 with LinearRegressionLSQRTrainer

use of org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer in project ignite by apache.

the class DistributedLinearRegressionWithLSQRTrainerAndNormalizationExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws InterruptedException {
    System.out.println();
    System.out.println(">>> Linear regression model over sparse distributed matrix API usage example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
        // because we create ignite cache internally.
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), SparseDistributedMatrixExample.class.getSimpleName(), () -> {
            IgniteCache<Integer, double[]> dataCache = getTestCache(ignite);
            System.out.println(">>> Create new normalization trainer object.");
            NormalizationTrainer<Integer, double[]> normalizationTrainer = new NormalizationTrainer<>();
            System.out.println(">>> Perform the training to get the normalization preprocessor.");
            NormalizationPreprocessor<Integer, double[]> preprocessor = normalizationTrainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), (k, v) -> Arrays.copyOfRange(v, 1, v.length), 4);
            System.out.println(">>> Create new linear regression trainer object.");
            LinearRegressionLSQRTrainer<Integer, double[]> trainer = new LinearRegressionLSQRTrainer<>();
            System.out.println(">>> Perform the training to get the model.");
            LinearRegressionModel mdl = trainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), preprocessor, (k, v) -> v[0], 4);
            System.out.println(">>> Linear regression model: " + mdl);
            System.out.println(">>> ---------------------------------");
            System.out.println(">>> | Prediction\t| Ground Truth\t|");
            System.out.println(">>> ---------------------------------");
            try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
                for (Cache.Entry<Integer, double[]> observation : observations) {
                    Integer key = observation.getKey();
                    double[] val = observation.getValue();
                    double groundTruth = val[0];
                    double prediction = mdl.apply(new DenseLocalOnHeapVector(preprocessor.apply(key, val)));
                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
                }
            }
            System.out.println(">>> ---------------------------------");
        });
        igniteThread.start();
        igniteThread.join();
    }
}

Example 2 with LinearRegressionLSQRTrainer

use of org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer in project ignite by apache.

the class DistributedLinearRegressionWithLSQRTrainerExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws InterruptedException {
    System.out.println();
    System.out.println(">>> Linear regression model over sparse distributed matrix API usage example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
        // because we create ignite cache internally.
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), SparseDistributedMatrixExample.class.getSimpleName(), () -> {
            IgniteCache<Integer, double[]> dataCache = getTestCache(ignite);
            System.out.println(">>> Create new linear regression trainer object.");
            LinearRegressionLSQRTrainer<Integer, double[]> trainer = new LinearRegressionLSQRTrainer<>();
            System.out.println(">>> Perform the training to get the model.");
            LinearRegressionModel mdl = trainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), (k, v) -> Arrays.copyOfRange(v, 1, v.length), (k, v) -> v[0], 4);
            System.out.println(">>> Linear regression model: " + mdl);
            System.out.println(">>> ---------------------------------");
            System.out.println(">>> | Prediction\t| Ground Truth\t|");
            System.out.println(">>> ---------------------------------");
            try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
                for (Cache.Entry<Integer, double[]> observation : observations) {
                    double[] val = observation.getValue();
                    double[] inputs = Arrays.copyOfRange(val, 1, val.length);
                    double groundTruth = val[0];
                    double prediction = mdl.apply(new DenseLocalOnHeapVector(inputs));
                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
                }
            }
            System.out.println(">>> ---------------------------------");
        });
        igniteThread.start();
        igniteThread.join();
    }
}