Examples with DatasetHelper org.apache.ignite.examples.ml.util.DatasetHelper used on opensource projects

Example 1 with DatasetHelper

use of org.apache.ignite.examples.ml.util.DatasetHelper in project ignite by apache.

the class CacheBasedDatasetExample 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(">>> Cache Based Dataset example started.");
        IgniteCache<Integer, Vector> persons = null;
        try {
            persons = createCache(ignite);
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(1, 2);
            // Creates a cache based simple dataset containing features and providing standard dataset API.
            try (SimpleDataset<?> dataset = DatasetFactory.createSimpleDataset(ignite, persons, vectorizer)) {
                new DatasetHelper(dataset).describe();
            }
            System.out.println(">>> Cache Based Dataset example completed.");
        } finally {
            persons.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 2 with DatasetHelper

use of org.apache.ignite.examples.ml.util.DatasetHelper in project ignite by apache.

the class MinMaxScalerExample 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(">>> MinMax preprocessing example started.");
        IgniteCache<Integer, Vector> data = null;
        try {
            data = createCache(ignite);
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(1, 2);
            // Defines second preprocessor that imputing features.
            Preprocessor<Integer, Vector> preprocessor = new MinMaxScalerTrainer<Integer, Vector>().fit(ignite, data, vectorizer);
            // Creates a cache based simple dataset containing features and providing standard dataset API.
            try (SimpleDataset<?> dataset = DatasetFactory.createSimpleDataset(ignite, data, preprocessor)) {
                new DatasetHelper(dataset).describe();
            }
            System.out.println(">>> MinMax preprocessing example completed.");
        } finally {
            data.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 3 with DatasetHelper

use of org.apache.ignite.examples.ml.util.DatasetHelper in project ignite by apache.

the class StandardScalerExample 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(">>> Standard scaler example started.");
        IgniteCache<Integer, Vector> data = null;
        try {
            data = createCache(ignite);
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(1, 2);
            // Defines second preprocessor that scales features.
            Preprocessor<Integer, Vector> preprocessor = new StandardScalerTrainer<Integer, Vector>().fit(ignite, data, vectorizer);
            // Creates a cache based simple dataset containing features and providing standard dataset API.
            try (SimpleDataset<?> dataset = DatasetFactory.createSimpleDataset(ignite, data, preprocessor)) {
                new DatasetHelper(dataset).describe();
            }
            System.out.println(">>> Standard scaler example completed.");
        } finally {
            data.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 4 with DatasetHelper

use of org.apache.ignite.examples.ml.util.DatasetHelper in project ignite by apache.

the class BinarizationExample 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(">>> Binarization example started.");
        IgniteCache<Integer, Vector> data = null;
        try {
            data = createCache(ignite);
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(1);
            // Defines second preprocessor that binarizes features.
            Preprocessor<Integer, Vector> preprocessor = new BinarizationTrainer<Integer, Vector>().withThreshold(40).fit(ignite, data, vectorizer);
            // Creates a cache based simple dataset containing features and providing standard dataset API.
            try (SimpleDataset<?> dataset = DatasetFactory.createSimpleDataset(ignite, data, preprocessor)) {
                new DatasetHelper(dataset).describe();
            }
            System.out.println(">>> Binarization example completed.");
        } finally {
            data.destroy();
        }
    } finally {
        System.out.flush();
    }
}

Example 5 with DatasetHelper

use of org.apache.ignite.examples.ml.util.DatasetHelper in project ignite by apache.

the class MaxAbsScalerExample 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(">>> Max abs example started.");
        IgniteCache<Integer, Vector> data = null;
        try {
            data = createCache(ignite);
            Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(1, 2);
            // Defines second preprocessor that imputing features.
            Preprocessor<Integer, Vector> preprocessor = new MaxAbsScalerTrainer<Integer, Vector>().fit(ignite, data, vectorizer);
            // Creates a cache based simple dataset containing features and providing standard dataset API.
            try (SimpleDataset<?> dataset = DatasetFactory.createSimpleDataset(ignite, data, preprocessor)) {
                new DatasetHelper(dataset).describe();
            }
            System.out.println(">>> Max abs example completed.");
        } finally {
            data.destroy();
        }
    } finally {
        System.out.flush();
    }
}