Examples with EuclideanDistance org.apache.ignite.ml.math.distances.EuclideanDistance used on opensource projects

Example 26 with EuclideanDistance

use of org.apache.ignite.ml.math.distances.EuclideanDistance in project ignite by apache.

the class KNNClassificationTest method testBinaryClassificationTest.

/**
 */
public void testBinaryClassificationTest() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    double[][] mtx = new double[][] { { 1.0, 1.0 }, { 1.0, 2.0 }, { 2.0, 1.0 }, { -1.0, -1.0 }, { -1.0, -2.0 }, { -2.0, -1.0 } };
    double[] lbs = new double[] { 1.0, 1.0, 1.0, 2.0, 2.0, 2.0 };
    LabeledDataset training = new LabeledDataset(mtx, lbs);
    KNNModel knnMdl = new KNNModel(3, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
    Vector firstVector = new DenseLocalOnHeapVector(new double[] { 2.0, 2.0 });
    assertEquals(knnMdl.apply(firstVector), 1.0);
    Vector secondVector = new DenseLocalOnHeapVector(new double[] { -2.0, -2.0 });
    assertEquals(knnMdl.apply(secondVector), 2.0);
}

Example 27 with EuclideanDistance

use of org.apache.ignite.ml.math.distances.EuclideanDistance in project ignite by apache.

the class KNNClassificationTest method testBinaryClassificationFarPointsWithWeightedStrategy.

/**
 */
public void testBinaryClassificationFarPointsWithWeightedStrategy() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    double[][] mtx = new double[][] { { 10.0, 10.0 }, { 10.0, 20.0 }, { -1, -1 }, { -2, -2 }, { -1.0, -2.0 }, { -2.0, -1.0 } };
    double[] lbs = new double[] { 1.0, 1.0, 1.0, 2.0, 2.0, 2.0 };
    LabeledDataset training = new LabeledDataset(mtx, lbs);
    KNNModel knnMdl = new KNNModel(3, new EuclideanDistance(), KNNStrategy.WEIGHTED, training);
    Vector vector = new DenseLocalOnHeapVector(new double[] { -1.01, -1.01 });
    assertEquals(knnMdl.apply(vector), 1.0);
}

Example 28 with EuclideanDistance

use of org.apache.ignite.ml.math.distances.EuclideanDistance in project ignite by apache.

the class KNNClassificationTest method testPredictOnIrisDataset.

/**
 */
public void testPredictOnIrisDataset() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    LabeledDataset training = loadDatasetFromTxt(KNN_IRIS_TXT, false);
    KNNModel knnMdl = new KNNModel(7, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
    Vector vector = new DenseLocalOnHeapVector(new double[] { 5.15, 3.55, 1.45, 0.25 });
    assertEquals(knnMdl.apply(vector), 1.0);
}

Example 29 with EuclideanDistance

use of org.apache.ignite.ml.math.distances.EuclideanDistance in project ignite by apache.

the class KNNMultipleLinearRegressionTest method testSimpleRegressionWithOneNeighbour.

/**
 */
public void testSimpleRegressionWithOneNeighbour() {
    y = new double[] { 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 };
    x = new double[6][];
    x[0] = new double[] { 0, 0, 0, 0, 0 };
    x[1] = new double[] { 2.0, 0, 0, 0, 0 };
    x[2] = new double[] { 0, 3.0, 0, 0, 0 };
    x[3] = new double[] { 0, 0, 4.0, 0, 0 };
    x[4] = new double[] { 0, 0, 0, 5.0, 0 };
    x[5] = new double[] { 0, 0, 0, 0, 6.0 };
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    LabeledDataset training = new LabeledDataset(x, y);
    KNNMultipleLinearRegression knnMdl = new KNNMultipleLinearRegression(1, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
    Vector vector = new SparseBlockDistributedVector(new double[] { 0, 0, 0, 5.0, 0.0 });
    System.out.println(knnMdl.apply(vector));
    Assert.assertEquals(15, knnMdl.apply(vector), 1E-12);
}

Example 30 with EuclideanDistance

use of org.apache.ignite.ml.math.distances.EuclideanDistance in project ignite by apache.

the class KNNMultipleLinearRegressionTest method testLongly.

/**
 */
public void testLongly() {
    y = new double[] { 60323, 61122, 60171, 61187, 63221, 63639, 64989, 63761, 66019, 68169, 66513, 68655, 69564, 69331, 70551 };
    x = new double[15][];
    x[0] = new double[] { 83.0, 234289, 2356, 1590, 107608, 1947 };
    x[1] = new double[] { 88.5, 259426, 2325, 1456, 108632, 1948 };
    x[2] = new double[] { 88.2, 258054, 3682, 1616, 109773, 1949 };
    x[3] = new double[] { 89.5, 284599, 3351, 1650, 110929, 1950 };
    x[4] = new double[] { 96.2, 328975, 2099, 3099, 112075, 1951 };
    x[5] = new double[] { 98.1, 346999, 1932, 3594, 113270, 1952 };
    x[6] = new double[] { 99.0, 365385, 1870, 3547, 115094, 1953 };
    x[7] = new double[] { 100.0, 363112, 3578, 3350, 116219, 1954 };
    x[8] = new double[] { 101.2, 397469, 2904, 3048, 117388, 1955 };
    x[9] = new double[] { 108.4, 442769, 2936, 2798, 120445, 1957 };
    x[10] = new double[] { 110.8, 444546, 4681, 2637, 121950, 1958 };
    x[11] = new double[] { 112.6, 482704, 3813, 2552, 123366, 1959 };
    x[12] = new double[] { 114.2, 502601, 3931, 2514, 125368, 1960 };
    x[13] = new double[] { 115.7, 518173, 4806, 2572, 127852, 1961 };
    x[14] = new double[] { 116.9, 554894, 4007, 2827, 130081, 1962 };
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    LabeledDataset training = new LabeledDataset(x, y);
    KNNMultipleLinearRegression knnMdl = new KNNMultipleLinearRegression(3, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
    Vector vector = new DenseLocalOnHeapVector(new double[] { 104.6, 419180, 2822, 2857, 118734, 1956 });
    System.out.println(knnMdl.apply(vector));
    Assert.assertEquals(67857, knnMdl.apply(vector), 2000);
}