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

Example 1 with Matrix

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

the class OffHeapMatrixExample method main.

/**
     * Executes example.
     *
     * @param args Command line arguments, none required.
     */
public static void main(String[] args) {
    System.out.println();
    System.out.println(">>> Off-heap 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 DenseLocalOffHeapMatrix(data.length, data[0].length);
    m.assign(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 DenseLocalOffHeapMatrix(data1.length, data1[0].length);
    Matrix m2 = new DenseLocalOffHeapMatrix(data2.length, data2[0].length);
    m1.assign(data1);
    m2.assign(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>>> Off-heap matrix API usage example completed.");
}

Example 2 with Matrix

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

the class SparseMatrixExample method main.

/**
     * Executes example.
     *
     * @param args Command line arguments, none required.
     */
public static void main(String[] args) {
    System.out.println();
    System.out.println(">>> Sparse 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 SparseLocalOnHeapMatrix(data.length, data[0].length);
    m.assign(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 SparseLocalOnHeapMatrix(data1.length, data1[0].length);
    Matrix m2 = new SparseLocalOnHeapMatrix(data2.length, data2[0].length);
    m1.assign(data1);
    m2.assign(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>>> Sparse matrix API usage example completed.");
}

Example 3 with Matrix

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

the class LUDecompositionExample method main.

/**
     * Executes example.
     *
     * @param args Command line arguments, none required.
     */
public static void main(String[] args) {
    System.out.println(">>> LU decomposition example started.");
    // Let's compute a LU decomposition for some (n x n) matrix m:
    // m = p l u, where
    // p is an (n x n) is a row-permutation matrix
    // l is a (n x n) lower triangular matrix
    // u is a (n x n) upper triangular matrix
    DenseLocalOnHeapMatrix m = new DenseLocalOnHeapMatrix(new double[][] { { 1.0d, 1.0d, -1.0d }, { 1.0d, -2.0d, 3.0d }, { 2.0d, 3.0d, 1.0d } });
    System.out.println("\n>>> Matrix m for decomposition: ");
    Tracer.showAscii(m);
    // This decomposition is useful when dealing with systems of linear equations.
    // (see https://en.wikipedia.org/wiki/LU_decomposition)
    // suppose we want to solve system
    // m x = b for various bs. Then after we computed LU decomposition, we can feed various bs
    // as a matrix of the form
    // (b1, b2, ..., bm)
    // to the method LUDecomposition::solve which returns solutions in the form
    // (sol1, sol2, ..., solm)
    LUDecomposition dec = new LUDecomposition(m);
    System.out.println("\n>>> Made decomposition.");
    System.out.println(">>> Matrix getL is ");
    Tracer.showAscii(dec.getL());
    System.out.println(">>> Matrix getU is ");
    Tracer.showAscii(dec.getU());
    System.out.println(">>> Matrix getP is ");
    Tracer.showAscii(dec.getP());
    Matrix bs = new DenseLocalOnHeapMatrix(new double[][] { { 4.0, -6.0, 7.0 }, { 1.0, 1.0, 1.0 } });
    System.out.println("\n>>> Matrix to solve: ");
    Tracer.showAscii(bs);
    Matrix sol = dec.solve(bs.transpose());
    System.out.println("\n>>> List of solutions: ");
    for (int i = 0; i < sol.columnSize(); i++) Tracer.showAscii(sol.viewColumn(i));
    System.out.println("\n>>> LU decomposition example completed.");
}

Example 4 with Matrix

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

the class OLSMultipleLinearRegressionTest method testHat.

/**
     * Test hat matrix computation
     */
@Test
public void testHat() {
    /*
         * This example is from "The Hat Matrix in Regression and ANOVA",
         * David C. Hoaglin and Roy E. Welsch,
         * The American Statistician, Vol. 32, No. 1 (Feb., 1978), pp. 17-22.
         *
         */
    double[] design = new double[] { 11.14, .499, 11.1, 12.74, .558, 8.9, 13.13, .604, 8.8, 11.51, .441, 8.9, 12.38, .550, 8.8, 12.60, .528, 9.9, 11.13, .418, 10.7, 11.7, .480, 10.5, 11.02, .406, 10.5, 11.41, .467, 10.7 };
    int nobs = 10;
    int nvars = 2;
    // Estimate the model
    OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
    mdl.newSampleData(design, nobs, nvars, new DenseLocalOnHeapMatrix());
    Matrix hat = mdl.calculateHat();
    // Reference data is upper half of symmetric hat matrix
    double[] refData = new double[] { .418, -.002, .079, -.274, -.046, .181, .128, .222, .050, .242, .242, .292, .136, .243, .128, -.041, .033, -.035, .004, .417, -.019, .273, .187, -.126, .044, -.153, .004, .604, .197, -.038, .168, -.022, .275, -.028, .252, .111, -.030, .019, -.010, -.010, .148, .042, .117, .012, .111, .262, .145, .277, .174, .154, .120, .168, .315, .148, .187 };
    // Check against reference data and verify symmetry
    int k = 0;
    for (int i = 0; i < 10; i++) {
        for (int j = i; j < 10; j++) {
            Assert.assertEquals(refData[k], hat.getX(i, j), 10e-3);
            Assert.assertEquals(hat.getX(i, j), hat.getX(j, i), 10e-12);
            k++;
        }
    }
    /*
         * Verify that residuals computed using the hat matrix are close to
         * what we get from direct computation, i.e. r = (I - H) y
         */
    double[] residuals = mdl.estimateResiduals();
    Matrix id = MatrixUtil.identityLike(hat, 10);
    double[] hatResiduals = id.minus(hat).times(mdl.getY()).getStorage().data();
    TestUtils.assertEquals(residuals, hatResiduals, 10e-12);
}

Example 5 with Matrix

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

the class VectorToMatrixTest method testLikeMatrix.

/** */
@Test
public void testLikeMatrix() {
    consumeSampleVectors((v, desc) -> {
        if (!availableForTesting(v))
            return;
        final Matrix matrix = v.likeMatrix(1, 1);
        Class<? extends Vector> key = v.getClass();
        Class<? extends Matrix> expMatrixType = typesMap.get(key);
        assertNotNull("Expect non-null matrix for " + key.getSimpleName() + " in " + desc, matrix);
        Class<? extends Matrix> actualMatrixType = matrix.getClass();
        assertTrue("Expected matrix type " + expMatrixType.getSimpleName() + " should be assignable from actual type " + actualMatrixType.getSimpleName() + " in " + desc, expMatrixType.isAssignableFrom(actualMatrixType));
        for (int rows : new int[] { 1, 2 }) for (int cols : new int[] { 1, 2 }) {
            final Matrix actualMatrix = v.likeMatrix(rows, cols);
            String details = "rows " + rows + " cols " + cols;
            assertNotNull("Expect non-null matrix for " + details + " in " + desc, actualMatrix);
            assertEquals("Unexpected number of rows in " + desc, rows, actualMatrix.rowSize());
            assertEquals("Unexpected number of cols in " + desc, cols, actualMatrix.columnSize());
        }
    });
}