Examples with DenseVector org.apache.ignite.ml.math.primitives.vector.impl.DenseVector used on opensource projects

Example 81 with DenseVector

use of org.apache.ignite.ml.math.primitives.vector.impl.DenseVector in project ignite by apache.

the class FrequencyEncoderPreprocessorTest method testApply.

/**
 * Tests {@code apply()} method.
 */
@Test
public void testApply() {
    Vector[] data = new Vector[] { new DenseVector(new Serializable[] { "1", "Moscow", "A" }), new DenseVector(new Serializable[] { "2", "Moscow", "B" }), new DenseVector(new Serializable[] { "2", "Moscow", "B" }) };
    Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(0, 1, 2);
    FrequencyEncoderPreprocessor<Integer, Vector> preprocessor = new FrequencyEncoderPreprocessor<Integer, Vector>(new HashMap[] { new HashMap() {

        {
            put("1", 0.33);
            put("2", 0.66);
        }
    }, new HashMap() {

        {
            put("Moscow", 1.0);
        }
    }, new HashMap() {

        {
            put("A", 0.33);
            put("B", 0.66);
        }
    } }, vectorizer, new HashSet() {

        {
            add(0);
            add(1);
            add(2);
        }
    });
    double[][] postProcessedData = new double[][] { { 0.33, 1.0, 0.33 }, { 0.66, 1.0, 0.66 }, { 0.66, 1.0, 0.66 } };
    for (int i = 0; i < data.length; i++) assertArrayEquals(postProcessedData[i], preprocessor.apply(i, data[i]).features().asArray(), 0.1);
}

Example 82 with DenseVector

use of org.apache.ignite.ml.math.primitives.vector.impl.DenseVector in project ignite by apache.

the class OneHotEncoderPreprocessorTest method testApplyWithUnknownCategorialValues.

/**
 * The {@code apply()} method is failed with UnknownCategorialFeatureValue exception.
 *
 * The reason is missed information in encodingValues.
 *
 * @see UnknownCategorialValueException
 */
@Test
public void testApplyWithUnknownCategorialValues() {
    Vector[] data = new Vector[] { new DenseVector(new Serializable[] { "1", "Moscow", "A" }), new DenseVector(new Serializable[] { "2", "Moscow", "A" }), new DenseVector(new Serializable[] { "2", "Moscow", "B" }) };
    Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(0, 1, 2);
    OneHotEncoderPreprocessor<Integer, Vector> preprocessor = new OneHotEncoderPreprocessor<Integer, Vector>(new HashMap[] { new HashMap() {

        {
            put("2", 0);
        }
    }, new HashMap() {

        {
            put("Moscow", 0);
        }
    }, new HashMap() {

        {
            put("A", 0);
            put("B", 1);
        }
    } }, vectorizer, new HashSet() {

        {
            add(0);
            add(1);
            add(2);
        }
    });
    double[][] postProcessedData = new double[][] { { 0.0, 1.0, 1.0, 1.0, 0.0 }, { 1.0, 0.0, 1.0, 1.0, 0.0 }, { 1.0, 0.0, 1.0, 0.0, 1.0 } };
    try {
        for (int i = 0; i < data.length; i++) assertArrayEquals(postProcessedData[i], preprocessor.apply(i, data[i]).features().asArray(), 1e-8);
        fail("UnknownCategorialFeatureValue");
    } catch (UnknownCategorialValueException e) {
        return;
    }
    fail("UnknownCategorialFeatureValue");
}

Example 83 with DenseVector

use of org.apache.ignite.ml.math.primitives.vector.impl.DenseVector in project ignite by apache.

the class OneHotEncoderPreprocessorTest method testApplyWithStringValues.

/**
 * Tests {@code apply()} method.
 */
@Test
public void testApplyWithStringValues() {
    Vector[] data = new Vector[] { new DenseVector(new Serializable[] { "1", "Moscow", "A" }), new DenseVector(new Serializable[] { "2", "Moscow", "A" }), new DenseVector(new Serializable[] { "2", "Moscow", "B" }) };
    Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(0, 1, 2);
    OneHotEncoderPreprocessor<Integer, Vector> preprocessor = new OneHotEncoderPreprocessor<Integer, Vector>(new HashMap[] { new HashMap() {

        {
            put("1", 1);
            put("2", 0);
        }
    }, new HashMap() {

        {
            put("Moscow", 0);
        }
    }, new HashMap() {

        {
            put("A", 0);
            put("B", 1);
        }
    } }, vectorizer, new HashSet() {

        {
            add(0);
            add(1);
            add(2);
        }
    });
    double[][] postProcessedData = new double[][] { { 0.0, 1.0, 1.0, 1.0, 0.0 }, { 1.0, 0.0, 1.0, 1.0, 0.0 }, { 1.0, 0.0, 1.0, 0.0, 1.0 } };
    for (int i = 0; i < data.length; i++) assertArrayEquals(postProcessedData[i], preprocessor.apply(i, data[i]).features().asArray(), 1e-8);
}

Example 84 with DenseVector

use of org.apache.ignite.ml.math.primitives.vector.impl.DenseVector in project ignite by apache.

the class VectorViewTest method setup.

/**
 */
@Before
public void setup() {
    parentVector = new DenseVector(MathTestConstants.STORAGE_SIZE);
    IntStream.range(0, MathTestConstants.STORAGE_SIZE).forEach(idx -> parentVector.set(idx, Math.random()));
    parentData = parentVector.getStorage().data().clone();
    testVector = new VectorView(parentVector, OFFSET, VIEW_LENGTH);
}

Example 85 with DenseVector

use of org.apache.ignite.ml.math.primitives.vector.impl.DenseVector in project ignite by apache.

the class LinearRegressionModelTest method testPredictOnAnObservationWithWrongCardinality.

/**
 */
@Test(expected = CardinalityException.class)
public void testPredictOnAnObservationWithWrongCardinality() {
    Vector weights = new DenseVector(new double[] { 2.0, 3.0 });
    LinearRegressionModel mdl = new LinearRegressionModel(weights, 1.0);
    Vector observation = new DenseVector(new double[] { 1.0 });
    mdl.predict(observation);
}