use of org.apache.ignite.ml.structures.LabeledDataset in project ignite by apache.
the class KNNMultipleLinearRegressionTest method testLonglyWithNormalization.
/**
*/
public void testLonglyWithNormalization() {
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);
final LabeledDataset normalizedTrainingDataset = (LabeledDataset) Normalizer.normalizeWithMiniMax(training);
KNNMultipleLinearRegression knnMdl = new KNNMultipleLinearRegression(5, new EuclideanDistance(), KNNStrategy.SIMPLE, normalizedTrainingDataset);
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);
}
use of org.apache.ignite.ml.structures.LabeledDataset in project ignite by apache.
the class LabeledDatasetTest method testAccessMethods.
/**
*/
public void testAccessMethods() {
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 };
final LabeledDataset dataset = new LabeledDataset(mtx, lbs, null, false);
assertEquals(dataset.colSize(), 2);
assertEquals(dataset.rowSize(), 6);
final LabeledVector<Vector, Double> row = (LabeledVector<Vector, Double>) dataset.getRow(0);
assertEquals(row.features().get(0), 1.0);
assertEquals(row.label(), 1.0);
dataset.setLabel(0, 2.0);
assertEquals(row.label(), 2.0);
}
use of org.apache.ignite.ml.structures.LabeledDataset in project ignite by apache.
the class LabeledDatasetTest method testLoadingFileWithIncorrectData.
/**
*/
public void testLoadingFileWithIncorrectData() {
IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
LabeledDataset training = loadDatasetFromTxt(IRIS_INCORRECT_TXT, false);
assertEquals(149, training.rowSize());
}
use of org.apache.ignite.ml.structures.LabeledDataset in project ignite by apache.
the class LabeledDatasetTest method testLabels.
/**
*/
public void testLabels() {
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 dataset = new LabeledDataset(mtx, lbs);
final double[] labels = dataset.labels();
for (int i = 0; i < lbs.length; i++) assertEquals(lbs[i], labels[i]);
}
use of org.apache.ignite.ml.structures.LabeledDataset in project ignite by apache.
the class LabeledDatasetTest method testExternalization.
/**
*/
@Override
public void testExternalization() {
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 dataset = new LabeledDataset(mtx, lbs);
this.externalizeTest(dataset);
}
Aggregations