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

Example 51 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());
        }
    });
}

Example 52 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 53 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 54 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 55 with Matrix

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

the class MLPGroupTrainerExample method main.

/**
 * Executes example.
 *
 * @param args Command line arguments, none required.
 */
public static void main(String[] args) throws InterruptedException {
    // IMPL NOTE based on MLPGroupTrainerTest#testXOR
    System.out.println(">>> Distributed multilayer perceptron example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
        // because we create ignite cache internally.
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), MLPGroupTrainerExample.class.getSimpleName(), () -> {
            int samplesCnt = 10000;
            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();
            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);
            IgniteCache<Integer, LabeledVector<Vector, Vector>> cache = LabeledVectorsCache.createNew(ignite);
            String cacheName = cache.getName();
            Random rnd = new Random(12345L);
            try (IgniteDataStreamer<Integer, LabeledVector<Vector, Vector>> streamer = ignite.dataStreamer(cacheName)) {
                streamer.perNodeBufferSize(100);
                for (int i = 0; i < samplesCnt; i++) {
                    int col = Math.abs(rnd.nextInt()) % 4;
                    streamer.addData(i, new LabeledVector<>(xorInputs.getCol(col), xorOutputs.getCol(col)));
                }
            }
            int totalCnt = 100;
            int failCnt = 0;
            MLPGroupUpdateTrainer<RPropParameterUpdate> trainer = MLPGroupUpdateTrainer.getDefault(ignite).withSyncPeriod(3).withTolerance(0.001).withMaxGlobalSteps(20);
            for (int i = 0; i < totalCnt; i++) {
                MLPGroupUpdateTrainerCacheInput trainerInput = new MLPGroupUpdateTrainerCacheInput(conf, new RandomInitializer(rnd), 6, cache, 10);
                MultilayerPerceptron mlp = trainer.train(trainerInput);
                Matrix predict = mlp.apply(xorInputs);
                System.out.println(">>> Prediction data at step " + i + " of total " + totalCnt + ":");
                Tracer.showAscii(predict);
                System.out.println("Difference estimate: " + xorOutputs.getRow(0).minus(predict.getRow(0)).kNorm(2));
                failCnt += closeEnough(xorOutputs.getRow(0), predict.getRow(0)) ? 0 : 1;
            }
            double failRatio = (double) failCnt / totalCnt;
            System.out.println("\n>>> Fail percentage: " + (failRatio * 100) + "%.");
            System.out.println("\n>>> Distributed multilayer perceptron example completed.");
        });
        igniteThread.start();
        igniteThread.join();
    }
}