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

Example 86 with DenseVector

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

the class LinearRegressionModelTest method testPredict.

/**
 */
@Test
public void testPredict() {
    Vector weights = new DenseVector(new double[] { 2.0, 3.0 });
    LinearRegressionModel mdl = new LinearRegressionModel(weights, 1.0);
    assertTrue(!mdl.toString().isEmpty());
    assertTrue(!mdl.toString(true).isEmpty());
    assertTrue(!mdl.toString(false).isEmpty());
    Vector observation = new DenseVector(new double[] { 1.0, 1.0 });
    TestUtils.assertEquals(1.0 + 2.0 * 1.0 + 3.0 * 1.0, mdl.predict(observation), PRECISION);
    observation = new DenseVector(new double[] { 2.0, 1.0 });
    TestUtils.assertEquals(1.0 + 2.0 * 2.0 + 3.0 * 1.0, mdl.predict(observation), PRECISION);
    observation = new DenseVector(new double[] { 1.0, 2.0 });
    TestUtils.assertEquals(1.0 + 2.0 * 1.0 + 3.0 * 2.0, mdl.predict(observation), PRECISION);
    observation = new DenseVector(new double[] { -2.0, 1.0 });
    TestUtils.assertEquals(1.0 - 2.0 * 2.0 + 3.0 * 1.0, mdl.predict(observation), PRECISION);
    observation = new DenseVector(new double[] { 1.0, -2.0 });
    TestUtils.assertEquals(1.0 + 2.0 * 1.0 - 3.0 * 2.0, mdl.predict(observation), PRECISION);
}

Example 87 with DenseVector

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

the class LogisticRegressionModelTest method testPredict.

/**
 */
@Test
public void testPredict() {
    Vector weights = new DenseVector(new double[] { 2.0, 3.0 });
    assertFalse(new LogisticRegressionModel(weights, 1.0).isKeepingRawLabels());
    assertEquals(0.1, new LogisticRegressionModel(weights, 1.0).withThreshold(0.1).threshold(), 0);
    assertTrue(!new LogisticRegressionModel(weights, 1.0).toString().isEmpty());
    assertTrue(!new LogisticRegressionModel(weights, 1.0).toString(true).isEmpty());
    assertTrue(!new LogisticRegressionModel(weights, 1.0).toString(false).isEmpty());
    verifyPredict(new LogisticRegressionModel(weights, 1.0).withRawLabels(true));
    verifyPredict(new LogisticRegressionModel(null, 1.0).withRawLabels(true).withWeights(weights));
    verifyPredict(new LogisticRegressionModel(weights, 1.0).withRawLabels(true).withThreshold(0.5));
    verifyPredict(new LogisticRegressionModel(weights, 0.0).withRawLabels(true).withIntercept(1.0));
}

Example 88 with DenseVector

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

the class LogisticRegressionModelTest method verifyPredict.

/**
 */
private void verifyPredict(LogisticRegressionModel mdl) {
    Vector observation = new DenseVector(new double[] { 1.0, 1.0 });
    TestUtils.assertEquals(sigmoid(1.0 + 2.0 * 1.0 + 3.0 * 1.0), mdl.predict(observation), PRECISION);
    observation = new DenseVector(new double[] { 2.0, 1.0 });
    TestUtils.assertEquals(sigmoid(1.0 + 2.0 * 2.0 + 3.0 * 1.0), mdl.predict(observation), PRECISION);
    observation = new DenseVector(new double[] { 1.0, 2.0 });
    TestUtils.assertEquals(sigmoid(1.0 + 2.0 * 1.0 + 3.0 * 2.0), mdl.predict(observation), PRECISION);
    observation = new DenseVector(new double[] { -2.0, 1.0 });
    TestUtils.assertEquals(sigmoid(1.0 - 2.0 * 2.0 + 3.0 * 1.0), mdl.predict(observation), PRECISION);
    observation = new DenseVector(new double[] { 1.0, -2.0 });
    TestUtils.assertEquals(sigmoid(1.0 + 2.0 * 1.0 - 3.0 * 2.0), mdl.predict(observation), PRECISION);
}

Example 89 with DenseVector

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

the class PipelineMdlTest method testPredict.

/**
 */
@Test
public void testPredict() {
    Vector weights = new DenseVector(new double[] { 2.0, 3.0 });
    verifyPredict(getMdl(new LogisticRegressionModel(weights, 1.0).withRawLabels(true)));
}

Example 90 with DenseVector

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

the class VectorUtils method concat.

/**
 * Concatenates given vectors.
 *
 * @param vs Other vectors.
 * @return Concatenation result.
 */
public static Vector concat(Vector... vs) {
    Vector res = vs.length == 0 ? new DenseVector() : vs[0];
    for (int i = 1; i < vs.length; i++) {
        Vector v = vs[i];
        res = concat(res, v);
    }
    return res;
}