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

Example 56 with Vector

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

the class JensenShannonDistance method compute.

/**
 * {@inheritDoc}
 */
@Override
public double compute(Vector a, Vector b) throws CardinalityException {
    Vector aNormalized = MatrixUtil.localCopyOf(a).divide(a.sum());
    Vector bNormalized = MatrixUtil.localCopyOf(b).divide(b.sum());
    Vector mean = aNormalized.plus(bNormalized).divide(2d);
    double js = aNormalized.map(mean, this::relativeEntropy).sum() + bNormalized.map(mean, this::relativeEntropy).sum();
    js /= Math.log(base);
    return Math.sqrt(js / 2d);
}

Example 57 with Vector

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

the class StackingTest method testSimpleStack.

/**
 * Tests simple stack training.
 */
@Test
public void testSimpleStack() {
    StackedDatasetTrainer<Vector, Vector, Double, LinearRegressionModel, Double> trainer = new StackedDatasetTrainer<>();
    UpdatesStrategy<SmoothParametrized, SimpleGDParameterUpdate> updatesStgy = new UpdatesStrategy<>(new SimpleGDUpdateCalculator(0.2), SimpleGDParameterUpdate.SUM_LOCAL, SimpleGDParameterUpdate.AVG);
    MLPArchitecture arch = new MLPArchitecture(2).withAddedLayer(10, true, Activators.RELU).withAddedLayer(1, false, Activators.SIGMOID);
    MLPTrainer<SimpleGDParameterUpdate> trainer1 = new MLPTrainer<>(arch, LossFunctions.MSE, updatesStgy, 3000, 10, 50, 123L);
    // Convert model trainer to produce Vector -> Vector model
    DatasetTrainer<AdaptableDatasetModel<Vector, Vector, Matrix, Matrix, MultilayerPerceptron>, Double> mlpTrainer = AdaptableDatasetTrainer.of(trainer1).beforeTrainedModel((Vector v) -> new DenseMatrix(v.asArray(), 1)).afterTrainedModel((Matrix mtx) -> mtx.getRow(0)).withConvertedLabels(VectorUtils::num2Arr);
    final double factor = 3;
    StackedModel<Vector, Vector, Double, LinearRegressionModel> mdl = trainer.withAggregatorTrainer(new LinearRegressionLSQRTrainer().withConvertedLabels(x -> x * factor)).addTrainer(mlpTrainer).withAggregatorInputMerger(VectorUtils::concat).withSubmodelOutput2VectorConverter(IgniteFunction.identity()).withVector2SubmodelInputConverter(IgniteFunction.identity()).withOriginalFeaturesKept(IgniteFunction.identity()).withEnvironmentBuilder(TestUtils.testEnvBuilder()).fit(getCacheMock(xor), parts, new DoubleArrayVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.LAST));
    assertEquals(0.0 * factor, mdl.predict(VectorUtils.of(0.0, 0.0)), 0.3);
    assertEquals(1.0 * factor, mdl.predict(VectorUtils.of(0.0, 1.0)), 0.3);
    assertEquals(1.0 * factor, mdl.predict(VectorUtils.of(1.0, 0.0)), 0.3);
    assertEquals(0.0 * factor, mdl.predict(VectorUtils.of(1.0, 1.0)), 0.3);
}

Example 58 with Vector

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

the class GDBTrainerTest method testClassifier.

/**
 */
private void testClassifier(BiFunction<GDBTrainer, Map<Integer, double[]>, IgniteModel<Vector, Double>> fitter) {
    int sampleSize = 100;
    double[] xs = new double[sampleSize];
    double[] ys = new double[sampleSize];
    for (int i = 0; i < sampleSize; i++) {
        xs[i] = i;
        ys[i] = ((int) (xs[i] / 10.0) % 2) == 0 ? -1.0 : 1.0;
    }
    Map<Integer, double[]> learningSample = new HashMap<>();
    for (int i = 0; i < sampleSize; i++) learningSample.put(i, new double[] { xs[i], ys[i] });
    GDBTrainer trainer = new GDBBinaryClassifierOnTreesTrainer(0.3, 500, 3, 0.0).withUsingIdx(true).withCheckConvergenceStgyFactory(new MeanAbsValueConvergenceCheckerFactory(0.3));
    IgniteModel<Vector, Double> mdl = fitter.apply(trainer, learningSample);
    int errorsCnt = 0;
    for (int j = 0; j < sampleSize; j++) {
        double x = xs[j];
        double y = ys[j];
        double p = mdl.predict(VectorUtils.of(x));
        if (p != y)
            errorsCnt++;
    }
    assertEquals(0, errorsCnt);
    assertTrue(mdl instanceof ModelsComposition);
    ModelsComposition composition = (ModelsComposition) mdl;
    composition.getModels().forEach(m -> assertTrue(m instanceof DecisionTreeModel));
    assertTrue(composition.getModels().size() < 500);
    assertTrue(composition.getPredictionsAggregator() instanceof WeightedPredictionsAggregator);
    trainer = trainer.withCheckConvergenceStgyFactory(new ConvergenceCheckerStubFactory());
    assertEquals(500, ((ModelsComposition) fitter.apply(trainer, learningSample)).getModels().size());
}

Example 59 with Vector

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

the class GDBTrainerTest method testUpdate.

/**
 */
@Test
public void testUpdate() {
    int sampleSize = 100;
    double[] xs = new double[sampleSize];
    double[] ys = new double[sampleSize];
    for (int i = 0; i < sampleSize; i++) {
        xs[i] = i;
        ys[i] = ((int) (xs[i] / 10.0) % 2) == 0 ? -1.0 : 1.0;
    }
    Map<Integer, double[]> learningSample = new HashMap<>();
    for (int i = 0; i < sampleSize; i++) learningSample.put(i, new double[] { xs[i], ys[i] });
    IgniteBiFunction<Integer, double[], Vector> fExtr = (k, v) -> VectorUtils.of(v[0]);
    IgniteBiFunction<Integer, double[], Double> lExtr = (k, v) -> v[1];
    GDBTrainer classifTrainer = new GDBBinaryClassifierOnTreesTrainer(0.3, 500, 3, 0.0).withUsingIdx(true).withCheckConvergenceStgyFactory(new MeanAbsValueConvergenceCheckerFactory(0.3));
    GDBTrainer regressTrainer = new GDBRegressionOnTreesTrainer(0.3, 500, 3, 0.0).withUsingIdx(true).withCheckConvergenceStgyFactory(new MeanAbsValueConvergenceCheckerFactory(0.3));
// testUpdate(learningSample, fExtr, lExtr, classifTrainer);
// testUpdate(learningSample, fExtr, lExtr, regressTrainer);
}

Example 60 with Vector

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

the class IgniteModelStorageUtilTest method testSaveRemoveSaveModel.

/**
 */
@Test
public void testSaveRemoveSaveModel() throws Exception {
    try (Ignite ignite = startGrid(cfg)) {
        IgniteModelStorageUtil.saveModel(ignite, i -> 0.42, "mdl");
        IgniteModelStorageUtil.removeModel(ignite, "mdl");
        IgniteModelStorageUtil.saveModel(ignite, i -> 0.43, "mdl");
        Model<Vector, Double> infMdl = IgniteModelStorageUtil.getModel(ignite, "mdl");
        assertEquals(0.43, infMdl.predict(VectorUtils.of()));
    }
}