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

Example 76 with DenseVector

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

the class LossFunctionsTest method testL2.

/**
 */
@Test
public void testL2() {
    IgniteDifferentiableVectorToDoubleFunction f = LossFunctions.L2.apply(new DenseVector(new double[] { 2.0, 1.0 }));
    assertNotNull(f);
    test(new double[] { 1.0, 3.0 }, f);
}

Example 77 with DenseVector

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

the class MLPTest method testXOR.

/**
 * Test that MLP with parameters that should produce function close to 'XOR' is close to 'XOR' on 'XOR' domain.
 */
@Test
public void testXOR() {
    MLPArchitecture conf = new MLPArchitecture(2).withAddedLayer(2, true, Activators.SIGMOID).withAddedLayer(1, true, Activators.SIGMOID);
    MultilayerPerceptron mlp1 = new MultilayerPerceptron(conf, new MLPConstInitializer(1, 2));
    mlp1.setWeights(1, new DenseMatrix(new double[][] { { 20.0, 20.0 }, { -20.0, -20.0 } }));
    mlp1.setBiases(1, new DenseVector(new double[] { -10.0, 30.0 }));
    MultilayerPerceptron mlp2 = mlp1.setWeights(2, new DenseMatrix(new double[][] { { 20.0, 20.0 } }));
    MultilayerPerceptron mlp = mlp2.setBiases(2, new DenseVector(new double[] { -30.0 }));
    Matrix input = new DenseMatrix(new double[][] { { 0.0, 0.0 }, { 0.0, 1.0 }, { 1.0, 0.0 }, { 1.0, 1.0 } });
    Matrix predict = mlp.predict(input);
    Matrix truth = new DenseMatrix(new double[][] { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } });
    TestUtils.checkIsInEpsilonNeighbourhood(predict.getRow(0), truth.getRow(0), 1E-4);
}

Example 78 with DenseVector

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

the class MLPTest method setParamsFlattening.

/**
 * Test methods related to parameters flattening.
 */
@Test
public void setParamsFlattening() {
    int inputSize = 3;
    int firstLayerNeuronsCnt = 2;
    int secondLayerNeurons = 1;
    DenseVector paramsVector = new DenseVector(new double[] { // First layer weight matrix.
    1.0, // First layer weight matrix.
    2.0, // First layer weight matrix.
    3.0, // First layer weight matrix.
    4.0, // First layer weight matrix.
    5.0, // First layer weight matrix.
    6.0, // Second layer weight matrix.
    7.0, // Second layer weight matrix.
    8.0, // Second layer biases.
    9.0 });
    DenseMatrix firstLayerWeights = new DenseMatrix(new double[][] { { 1.0, 2.0, 3.0 }, { 4.0, 5.0, 6.0 } });
    DenseMatrix secondLayerWeights = new DenseMatrix(new double[][] { { 7.0, 8.0 } });
    DenseVector secondLayerBiases = new DenseVector(new double[] { 9.0 });
    MLPArchitecture conf = new MLPArchitecture(inputSize).withAddedLayer(firstLayerNeuronsCnt, false, Activators.SIGMOID).withAddedLayer(secondLayerNeurons, true, Activators.SIGMOID);
    MultilayerPerceptron mlp = new MultilayerPerceptron(conf, new MLPConstInitializer(100, 200));
    mlp.setParameters(paramsVector);
    Assert.assertEquals(paramsVector, mlp.parameters());
    Assert.assertEquals(mlp.weights(1), firstLayerWeights);
    Assert.assertEquals(mlp.weights(2), secondLayerWeights);
    Assert.assertEquals(mlp.biases(2), secondLayerBiases);
}

Example 79 with DenseVector

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

the class StringEncoderPreprocessorTest 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);
    StringEncoderPreprocessor<Integer, Vector> preprocessor = new StringEncoderPreprocessor<Integer, Vector>(new HashMap[] { new HashMap() {

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

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

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

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

Example 80 with DenseVector

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

the class TargetEncoderPreprocessorTest 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[] { "3", "Moscow", "B" }) };
    Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<>(0, 1, 2);
    TargetEncoderPreprocessor<Integer, Vector> preprocessor = new TargetEncoderPreprocessor<>(new TargetEncodingMeta[] { // feature 0
    new TargetEncodingMeta().withGlobalMean(0.5).withCategoryMean(new HashMap<String, Double>() {

        {
            // category "1" avg mean = 1.0
            put("1", 1.0);
            // category "2" avg mean = 0.0
            put("2", 0.0);
        }
    }), // feature 1
    new TargetEncodingMeta().withGlobalMean(0.1).withCategoryMean(new HashMap<String, Double>() {
    }), // feature 2
    new TargetEncodingMeta().withGlobalMean(0.1).withCategoryMean(new HashMap<String, Double>() {

        {
            // category "A" avg mean 1.0
            put("A", 1.0);
            // category "B" avg mean 2.0
            put("B", 2.0);
        }
    }) }, vectorizer, new HashSet<Integer>() {

        {
            add(0);
            add(1);
            add(2);
        }
    });
    double[][] postProcessedData = new double[][] { { // "1" contains in dict => use category mean 1.0
    1.0, // "Moscow" not contains in dict => use global 0.1
    0.1, // "A" contains in dict => use category mean 1.0
    1.0 }, { // "2" contains in dict => use category mean 0.0
    0.0, // "Moscow" not contains in dict => use global 0.1
    0.1, // "B" contains in dict => use category mean 2.0
    2.0 }, { // "3" not contains in dict => use global mean 0.5
    0.5, // "Moscow" not contains in dict => use global 0.1
    0.1, // "B" contains in dict => use category mean 2.0
    2.0 } };
    for (int i = 0; i < data.length; i++) assertArrayEquals(postProcessedData[i], preprocessor.apply(i, data[i]).features().asArray(), 1e-8);
}