Examples with BaseSparseNDArray org.nd4j.linalg.api.ndarray.BaseSparseNDArray used on opensource projects

Example 1 with BaseSparseNDArray

use of org.nd4j.linalg.api.ndarray.BaseSparseNDArray in project nd4j by deeplearning4j.

the class SparseBaseLevel1 method axpy.

/**
 * Adds a scalar multiple of compressed sparse vector to a full-storage vector.
 *
 * @param n The number of element
 * @param alpha
 * @param x a sparse vector
 * @param y a dense vector
 */
@Override
public void axpy(int n, double alpha, INDArray x, INDArray y) {
    BaseSparseNDArray sparseX = (BaseSparseNDArray) x;
    DataBuffer pointers = sparseX.getVectorCoordinates();
    switch(x.data().dataType()) {
        case DOUBLE:
            DefaultOpExecutioner.validateDataType(DataBuffer.Type.DOUBLE, x);
            DefaultOpExecutioner.validateDataType(DataBuffer.Type.DOUBLE, y);
            daxpyi(n, alpha, x, pointers, y);
            break;
        case FLOAT:
            DefaultOpExecutioner.validateDataType(DataBuffer.Type.FLOAT, x);
            DefaultOpExecutioner.validateDataType(DataBuffer.Type.FLOAT, y);
            saxpyi(n, alpha, x, pointers, y);
            break;
        case HALF:
            DefaultOpExecutioner.validateDataType(DataBuffer.Type.HALF, x);
            DefaultOpExecutioner.validateDataType(DataBuffer.Type.HALF, y);
            haxpyi(n, alpha, x, pointers, y);
            break;
        default:
            throw new UnsupportedOperationException();
    }
}

Example 2 with BaseSparseNDArray

use of org.nd4j.linalg.api.ndarray.BaseSparseNDArray in project nd4j by deeplearning4j.

the class SparseBaseLevel1 method dot.

/**
 * computes a vector-vector dot product.
 *
 * @param n number of accessed element
 * @param alpha
 * @param X an INDArray
 * @param Y an INDArray
 * @return the vector-vector dot product of X and Y
 */
@Override
public double dot(int n, double alpha, INDArray X, INDArray Y) {
    if (X instanceof BaseSparseNDArray) {
        BaseSparseNDArray sparseX = (BaseSparseNDArray) X;
        DataBuffer pointers = sparseX.getVectorCoordinates();
        switch(X.data().dataType()) {
            case DOUBLE:
                DefaultOpExecutioner.validateDataType(DataBuffer.Type.DOUBLE, X, Y);
                return ddoti(n, X, pointers, Y);
            case FLOAT:
                DefaultOpExecutioner.validateDataType(DataBuffer.Type.FLOAT, X, Y);
                return sdoti(n, X, pointers, Y);
            case HALF:
                DefaultOpExecutioner.validateDataType(DataBuffer.Type.HALF, X, Y);
                return hdoti(n, X, pointers, Y);
            default:
        }
    }
    throw new UnsupportedOperationException();
}

Example 3 with BaseSparseNDArray

use of org.nd4j.linalg.api.ndarray.BaseSparseNDArray in project nd4j by deeplearning4j.

the class SparseCSRLevel1Test method shouldComputeRotWithFullVector.

@Test
public void shouldComputeRotWithFullVector() {
    // try with dense vectors to get the expected result
    /*
            INDArray temp1 = Nd4j.create( new double[] {1 ,2, 3, 4});
            INDArray temp2 = Nd4j.create( new double[] {1 ,2, 3, 4});
            System.out.println("before: " + temp1.data() + " " + temp2.data());
            Nd4j.getBlasWrapper().level1().rot(temp1.length(), temp1, temp2, 1, 2);
            System.out.println("after: " + temp1.data() + " " + temp2.data());
        */
    // before: [1.0,2.0,3.0,4.0]  [1.0,2.0,3.0,4.0]
    // after: [3.0,6.0,0.0,12.0] [-1.0,-2.0,-3.0,-4.0]
    int[] cols = { 0, 1, 2, 3 };
    double[] values = { 1, 2, 3, 4 };
    INDArray sparseVec = Nd4j.createSparseCSR(values, cols, pointerB, pointerE, shape);
    INDArray vec = Nd4j.create(new double[] { 1, 2, 3, 4 });
    Nd4j.getBlasWrapper().level1().rot(vec.length(), sparseVec, vec, 1, 2);
    INDArray expectedSparseVec = Nd4j.createSparseCSR(new double[] { 3, 6, 9, 12 }, new int[] { 0, 1, 2, 3 }, new int[] { 0 }, new int[] { 4 }, new int[] { 1, 4 });
    INDArray expectedVec = Nd4j.create(new double[] { -1, -2, -3, -4 });
    assertEquals(getFailureMessage(), expectedSparseVec.data(), sparseVec.data());
    assertEquals(getFailureMessage(), expectedVec, vec);
    if (expectedSparseVec.isSparse() && sparseVec.isSparse()) {
        BaseSparseNDArray vec2 = ((BaseSparseNDArray) expectedSparseVec);
        BaseSparseNDArray vecSparse2 = ((BaseSparseNDArray) sparseVec);
        assertEquals(getFailureMessage(), vec2.getVectorCoordinates(), vecSparse2);
    }
}

Example 4 with BaseSparseNDArray

use of org.nd4j.linalg.api.ndarray.BaseSparseNDArray in project nd4j by deeplearning4j.

the class SparseCOOLevel1Test method shouldComputeRotWithFullVector.

@Test
public void shouldComputeRotWithFullVector() {
    // try with dense vectors to get the expected result
    /*
            INDArray temp1 = Nd4j.create( new double[] {1 ,2, 3, 4});
            INDArray temp2 = Nd4j.create( new double[] {1 ,2, 3, 4});
            System.out.println("before: " + temp1.data() + " " + temp2.data());
            Nd4j.getBlasWrapper().level1().rot(temp1.length(), temp1, temp2, 1, 2);
            System.out.println("after: " + temp1.data() + " " + temp2.data());
        */
    // before: [1.0,2.0,3.0,4.0]  [1.0,2.0,3.0,4.0]
    // after: [3.0,6.0,0.0,12.0] [-1.0,-2.0,-3.0,-4.0]
    int[] cols = { 0, 1, 2, 3 };
    double[] values = { 1, 2, 3, 4 };
    INDArray sparseVec = Nd4j.createSparseCOO(data, indexes, shape);
    INDArray vec = Nd4j.create(new double[] { 1, 2, 3, 4 });
    Nd4j.getBlasWrapper().level1().rot(vec.length(), sparseVec, vec, 1, 2);
    INDArray expectedSparseVec = Nd4j.createSparseCSR(new double[] { 3, 6, 9, 12 }, new int[] { 0, 1, 2, 3 }, new int[] { 0 }, new int[] { 4 }, new int[] { 1, 4 });
    INDArray expectedVec = Nd4j.create(new double[] { -1, -2, -3, -4 });
    assertEquals(getFailureMessage(), expectedSparseVec.data(), sparseVec.data());
    assertEquals(getFailureMessage(), expectedVec, vec);
    if (expectedSparseVec.isSparse() && sparseVec.isSparse()) {
        BaseSparseNDArray vec2 = ((BaseSparseNDArray) expectedSparseVec);
        BaseSparseNDArray vecSparse2 = ((BaseSparseNDArray) sparseVec);
        assertEquals(getFailureMessage(), vec2.getVectorCoordinates(), vecSparse2);
    }
}