Java Code Examples for org.nd4j.linalg.api.shape.Shape#isRowVectorShape()

protected void assertSlice(INDArray put, long slice) {
    Preconditions.checkArgument(slice < slices(), "Invalid slice specified: slice %s must be in range 0 (inclusive) to numSlices=%s (exclusive)", slice, slices());
    long[] sliceShape = put.shape();
    if (Shape.isRowVectorShape(sliceShape)) {
        return;
    } else {
        long[] requiredShape = ArrayUtil.removeIndex(shape(), 0);

        //no need to compare for scalar; primarily due to shapes either being [1] or length 0
        if (put.isScalar())
            return;

        if (isVector() && put.isVector() && put.length() < length())
            return;
        //edge case for column vectors
        if (Shape.isColumnVectorShape(sliceShape))
            return;
        if (!Shape.shapeEquals(sliceShape, requiredShape) && !Shape.isRowVectorShape(requiredShape)
                && !Shape.isRowVectorShape(sliceShape))
            throw new IllegalStateException(String.format("Invalid shape size of %s . Should have been %s ",
                    Arrays.toString(sliceShape), Arrays.toString(requiredShape)));
    }
}
 

private void applyBroadcastOp(INDArray vector, final char operation) {
    Nd4j.getCompressor().autoDecompress(this);
    int alongDimension = Shape.isRowVectorShape(vector.shape()) ? 1 : 0;

    // FIXME: probably this is wrong, because strict equality is always false in current DataBuffer mechanics
    if (this.data() == vector.data())
        vector = vector.dup();
    switch (operation) {
        case 'a':
            Nd4j.getExecutioner().exec(new BroadcastAddOp(this, vector, this, alongDimension), alongDimension);
            return;
        case 's':
            Nd4j.getExecutioner().exec(new BroadcastSubOp(this, vector, this, alongDimension), alongDimension);
            return;
        case 'm':
            Nd4j.getExecutioner().exec(new BroadcastMulOp(this, vector, this, alongDimension), alongDimension);
            return;
        case 'd':
            Nd4j.getExecutioner().exec(new BroadcastDivOp(this, vector, this, alongDimension), alongDimension);
            return;
        case 'h':
            Nd4j.getExecutioner().exec(new BroadcastRSubOp(this, vector, this, alongDimension), alongDimension);
            return;
        case 't':
            Nd4j.getExecutioner().exec(new BroadcastRDivOp(this, vector, this, alongDimension), alongDimension);
            return;
        case 'p':
            Nd4j.getExecutioner().exec(new BroadcastCopyOp(this, vector, this, alongDimension), alongDimension);
            return;
        default:
            throw new UnsupportedOperationException("Unknown operation: " + operation);
    }
}
 

/**
 * Number of columns (shape[1]), throws an exception when
 * called when not 2d
 *
 * @return the number of columns in the array (only 2d)
 */
@Override
public int columns() {
    // FIXME: int cast
    if (isMatrix())
        return (int) size(1);
    else if (Shape.isColumnVectorShape(shape())) {
        return 1;
    } else if (Shape.isRowVectorShape(shape())) {
        return (int) length();
    }
    throw new IllegalStateException("Rank is [" + rank() + "]; columns() call is not valid");


}
 

/**
 * Returns the number of rows
 * in the array (only 2d) throws an exception when
 * called when not 2d
 *
 * @return the number of rows in the matrix
 */
@Override
public int rows() {
    // FIXME:
    if (isMatrix())
        return (int) size(0);
    else if (Shape.isRowVectorShape(shape())) {
        return 1;
    } else if (Shape.isColumnVectorShape(shape())) {
        return (int) length();
    }

    throw new IllegalStateException("Rank is " + rank() + " rows() call is not valid");
}
 

private void applyBroadcastOp(INDArray vector, final char operation) {
    Nd4j.getCompressor().autoDecompress(this);
    int alongDimension = Shape.isRowVectorShape(vector.shape()) ? 1 : 0;

    // FIXME: probably this is wrong, because strict equality is always false in current DataBuffer mechanics
    if (this.data() == vector.data())
        vector = vector.dup();
    switch (operation) {
        case 'a':
            Nd4j.getExecutioner().exec(new BroadcastAddOp(this, vector, this, alongDimension));
            return;
        case 's':
            Nd4j.getExecutioner().exec(new BroadcastSubOp(this, vector, this, alongDimension));
            return;
        case 'm':
            Nd4j.getExecutioner().exec(new BroadcastMulOp(this, vector, this, alongDimension));
            return;
        case 'd':
            Nd4j.getExecutioner().exec(new BroadcastDivOp(this, vector, this, alongDimension));
            return;
        case 'h':
            Nd4j.getExecutioner().exec(new BroadcastRSubOp(this, vector, this, alongDimension));
            return;
        case 't':
            Nd4j.getExecutioner().exec(new BroadcastRDivOp(this, vector, this, alongDimension));
            return;
        case 'p':
            Nd4j.getExecutioner().exec(new BroadcastCopyOp(this, vector, this, alongDimension));
            return;
        default:
            throw new UnsupportedOperationException("Unknown operation: " + operation);
    }
}
 

@Override
public int columns() {
    if (isMatrix())
        return (int) size(1);
    else if (Shape.isColumnVectorShape(shape())) {
        return 1;
    } else if (Shape.isRowVectorShape(shape())) {
        return (int) length();
    }
    throw new IllegalStateException("Rank is [" + rank() + "]; columns() call is not valid");


}
 

@Override
public int rows() {
    if (isMatrix())
        return (int) size(0);
    else if (Shape.isRowVectorShape(shape())) {
        return 1;
    } else if (Shape.isColumnVectorShape(shape())) {
        return (int) length();
    }

    throw new IllegalStateException("Rank is " + rank() + " rows() call is not valid");
}
 

@Override
public INDArray tensorAlongDimension(int index, int... dimension) {
    if (dimension == null || dimension.length == 0)
        throw new IllegalArgumentException("Invalid input: dimensions not specified (null or length 0)");

    if (dimension.length >= rank()  || dimension.length == 1 && dimension[0] == Integer.MAX_VALUE)
        return this;
    for (int i = 0; i < dimension.length; i++)
        if (dimension[i] < 0)
            dimension[i] += rank();

    //dedup
    if (dimension.length > 1)
        dimension = Ints.toArray(new ArrayList<>(new TreeSet<>(Ints.asList(dimension))));

    if (dimension.length > 1) {
        Arrays.sort(dimension);
    }

    long tads = tensorssAlongDimension(dimension);
    if (index >= tads)
        throw new IllegalArgumentException("Illegal index " + index + " out of tads " + tads);


    if (dimension.length == 1) {
        if (dimension[0] == 0 && isColumnVector()) {
            return this.transpose();
        } else if (dimension[0] == 1 && isRowVector()) {
            return this;
        }
    }

    Pair<DataBuffer, DataBuffer> tadInfo =
            Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(this, dimension);
    DataBuffer shapeInfo = tadInfo.getFirst();
    val shape = Shape.shape(shapeInfo);
    val stride = Shape.stride(shapeInfo).asLong();
    long offset = offset() + tadInfo.getSecond().getLong(index);
    INDArray toTad = Nd4j.create(data(), shape, stride, offset);
    BaseNDArray baseNDArray = (BaseNDArray) toTad;

    //preserve immutability
    char newOrder = Shape.getOrder(shape, stride, 1);

    int ews = baseNDArray.shapeInfoDataBuffer().getInt(baseNDArray.shapeInfoDataBuffer().length() - 2);

    //TAD always calls permute. Permute EWS is always -1. This is not true
    // for row vector shapes though.
    if (!Shape.isRowVectorShape(baseNDArray.shapeInfoDataBuffer()))
        ews = -1;

    // we create new shapeInfo with possibly new ews & order
    /**
     * NOTE HERE THAT ZERO IS PRESET FOR THE OFFSET AND SHOULD STAY LIKE THAT.
     * Zero is preset for caching purposes.
     * We don't actually use the offset defined in the
     * shape info data buffer.
     * We calculate and cache the offsets separately.
     *
     */
    baseNDArray.setShapeInformation(
            Nd4j.getShapeInfoProvider().createShapeInformation(shape, stride, 0, ews, newOrder));

    return toTad;
}