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

Example 61 with Matrix

use of org.apache.ignite.ml.math.Matrix in project ignite by apache.

the class AbstractMultipleLinearRegressionTest method testNewSample.

/**
     * Verifies that newSampleData methods consistently insert unitary columns
     * in design matrix.  Confirms the fix for MATH-411.
     */
@Test
public void testNewSample() {
    double[] design = new double[] { 1, 19, 22, 33, 2, 20, 30, 40, 3, 25, 35, 45, 4, 27, 37, 47 };
    double[] y = new double[] { 1, 2, 3, 4 };
    double[][] x = new double[][] { { 19, 22, 33 }, { 20, 30, 40 }, { 25, 35, 45 }, { 27, 37, 47 } };
    AbstractMultipleLinearRegression regression = createRegression();
    regression.newSampleData(design, 4, 3, new DenseLocalOnHeapMatrix());
    Matrix flatX = regression.getX().copy();
    Vector flatY = regression.getY().copy();
    regression.newXSampleData(new DenseLocalOnHeapMatrix(x));
    regression.newYSampleData(new DenseLocalOnHeapVector(y));
    Assert.assertEquals(flatX, regression.getX());
    Assert.assertEquals(flatY, regression.getY());
    // No intercept
    regression.setNoIntercept(true);
    regression.newSampleData(design, 4, 3, new DenseLocalOnHeapMatrix());
    flatX = regression.getX().copy();
    flatY = regression.getY().copy();
    regression.newXSampleData(new DenseLocalOnHeapMatrix(x));
    regression.newYSampleData(new DenseLocalOnHeapVector(y));
    Assert.assertEquals(flatX, regression.getX());
    Assert.assertEquals(flatY, regression.getY());
}

Example 62 with Matrix

use of org.apache.ignite.ml.math.Matrix in project ignite by apache.

the class OLSMultipleLinearRegressionTest method testPerfectFit.

/** */
@Test
public void testPerfectFit() {
    double[] betaHat = regression.estimateRegressionParameters();
    TestUtils.assertEquals(new double[] { 11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0 }, betaHat, 1e-13);
    double[] residuals = regression.estimateResiduals();
    TestUtils.assertEquals(new double[] { 0d, 0d, 0d, 0d, 0d, 0d }, residuals, 1e-13);
    Matrix errors = regression.estimateRegressionParametersVariance();
    final double[] s = { 1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0 };
    Matrix refVar = new DenseLocalOnHeapMatrix(s.length, s.length);
    for (int i = 0; i < refVar.rowSize(); i++) for (int j = 0; j < refVar.columnSize(); j++) {
        if (i == 0) {
            refVar.setX(i, j, s[j]);
            continue;
        }
        double x = s[i] * s[j];
        refVar.setX(i, j, (i == j) ? 2 * x : x);
    }
    Assert.assertEquals(0.0, TestUtils.maximumAbsoluteRowSum(errors.minus(refVar)), 5.0e-16 * TestUtils.maximumAbsoluteRowSum(refVar));
    Assert.assertEquals(1, ((OLSMultipleLinearRegression) regression).calculateRSquared(), 1E-12);
}

Example 63 with Matrix

use of org.apache.ignite.ml.math.Matrix in project ignite by apache.

the class OLSMultipleLinearRegressionTest method testNewSample2.

/**
     * Verifies that setting X and Y separately has the same effect as newSample(X,Y).
     */
@Test
public void testNewSample2() {
    double[] y = new double[] { 1, 2, 3, 4 };
    double[][] x = new double[][] { { 19, 22, 33 }, { 20, 30, 40 }, { 25, 35, 45 }, { 27, 37, 47 } };
    OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
    regression.newSampleData(new DenseLocalOnHeapVector(y), new DenseLocalOnHeapMatrix(x));
    Matrix combinedX = regression.getX().copy();
    Vector combinedY = regression.getY().copy();
    regression.newXSampleData(new DenseLocalOnHeapMatrix(x));
    regression.newYSampleData(new DenseLocalOnHeapVector(y));
    Assert.assertEquals(combinedX, regression.getX());
    Assert.assertEquals(combinedY, regression.getY());
    // No intercept
    regression.setNoIntercept(true);
    regression.newSampleData(new DenseLocalOnHeapVector(y), new DenseLocalOnHeapMatrix(x));
    combinedX = regression.getX().copy();
    combinedY = regression.getY().copy();
    regression.newXSampleData(new DenseLocalOnHeapMatrix(x));
    regression.newYSampleData(new DenseLocalOnHeapVector(y));
    Assert.assertEquals(combinedX, regression.getX());
    Assert.assertEquals(combinedY, regression.getY());
}

Example 64 with Matrix

use of org.apache.ignite.ml.math.Matrix in project ignite by apache.

the class MatrixCustomStorageExample method main.

/**
 * Executes example.
 *
 * @param args Command line arguments, none required.
 */
public static void main(String[] args) {
    System.out.println();
    System.out.println(">>> Matrix API usage example started.");
    System.out.println("\n>>> Creating a matrix to be transposed.");
    double[][] data = new double[][] { { 1, 2, 3 }, { 4, 5, 6 } };
    Matrix m = new MatrixCustomStorage(data);
    Matrix transposed = m.transpose();
    System.out.println(">>> Matrix: ");
    MatrixExampleUtil.print(m);
    System.out.println(">>> Transposed matrix: ");
    MatrixExampleUtil.print(transposed);
    MatrixExampleUtil.verifyTransposition(m, transposed);
    System.out.println("\n>>> Creating matrices to be multiplied.");
    double[][] data1 = new double[][] { { 1, 2 }, { 3, 4 } };
    double[][] data2 = new double[][] { { 5, 6 }, { 7, 8 } };
    Matrix m1 = new MatrixCustomStorage(data1);
    Matrix m2 = new MatrixCustomStorage(data2);
    Matrix mult = m1.times(m2);
    System.out.println(">>> First matrix: ");
    MatrixExampleUtil.print(m1);
    System.out.println(">>> Second matrix: ");
    MatrixExampleUtil.print(m2);
    System.out.println(">>> Matrix product: ");
    MatrixExampleUtil.print(mult);
    System.out.println("\n>>> Calculating matrices determinants.");
    double det1 = m1.determinant();
    double det2 = m2.determinant();
    double detMult = mult.determinant();
    boolean detMultIsAsExp = Math.abs(detMult - det1 * det2) < 0.0001d;
    System.out.println(">>> First matrix determinant: [" + det1 + "].");
    System.out.println(">>> Second matrix determinant: [" + det2 + "].");
    System.out.println(">>> Matrix product determinant: [" + detMult + "], equals product of two other matrices determinants: [" + detMultIsAsExp + "].");
    System.out.println("Determinant of product matrix [" + detMult + "] should be equal to product of determinants [" + (det1 * det2) + "].");
    System.out.println("\n>>> Matrix API usage example completed.");
}

Example 65 with Matrix

use of org.apache.ignite.ml.math.Matrix in project ignite by apache.

the class MLPLocalTrainerExample method main.

/**
 * Executes example.
 *
 * @param args Command line arguments, none required.
 */
public static void main(String[] args) {
    // IMPL NOTE based on MLPLocalTrainerTest#testXORRProp
    System.out.println(">>> Local multilayer perceptron example started.");
    Matrix xorInputs = new DenseLocalOnHeapMatrix(new double[][] { { 0.0, 0.0 }, { 0.0, 1.0 }, { 1.0, 0.0 }, { 1.0, 1.0 } }, StorageConstants.ROW_STORAGE_MODE).transpose();
    System.out.println("\n>>> Input data:");
    Tracer.showAscii(xorInputs);
    Matrix xorOutputs = new DenseLocalOnHeapMatrix(new double[][] { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } }, StorageConstants.ROW_STORAGE_MODE).transpose();
    MLPArchitecture conf = new MLPArchitecture(2).withAddedLayer(10, true, Activators.RELU).withAddedLayer(1, false, Activators.SIGMOID);
    SimpleMLPLocalBatchTrainerInput trainerInput = new SimpleMLPLocalBatchTrainerInput(conf, new Random(1234L), xorInputs, xorOutputs, 4);
    System.out.println("\n>>> Perform training.");
    MultilayerPerceptron mlp = new MLPLocalBatchTrainer<>(LossFunctions.MSE, RPropUpdateCalculator::new, 0.0001, 16000).train(trainerInput);
    System.out.println("\n>>> Apply model.");
    Matrix predict = mlp.apply(xorInputs);
    System.out.println("\n>>> Predicted data:");
    Tracer.showAscii(predict);
    System.out.println("\n>>> Reference expected data:");
    Tracer.showAscii(xorOutputs);
    System.out.println("\n>>> Difference estimate: " + xorOutputs.getRow(0).minus(predict.getRow(0)).kNorm(2));
    System.out.println("\n>>> Local multilayer perceptron example completed.");
}