Examples with DataBuffer org.nd4j.linalg.api.buffer.DataBuffer used on opensource projects

Example 16 with DataBuffer

use of org.nd4j.linalg.api.buffer.DataBuffer in project nd4j by deeplearning4j.

the class BaseShapeInfoProvider method createShapeInformation.

@Override
public Pair<DataBuffer, int[]> createShapeInformation(int[] shape, int[] stride, long offset, int elementWiseStride, char order) {
    DataBuffer buffer = Shape.createShapeInformation(shape, stride, offset, elementWiseStride, order);
    buffer.setConstant(true);
    return Pair.create(buffer, buffer.asInt());
}

Example 17 with DataBuffer

use of org.nd4j.linalg.api.buffer.DataBuffer in project nd4j by deeplearning4j.

the class BaseSparseNDArray method reallocate.

protected DataBuffer reallocate(DataBuffer buffer) {
    int newSize = (int) buffer.length() * 2;
    DataBuffer newBuffer = Nd4j.createBuffer(newSize);
    switch(buffer.dataType()) {
        case INT:
            newBuffer.setData(buffer.asInt());
            break;
        case DOUBLE:
            newBuffer.setData(buffer.asDouble());
            break;
        case FLOAT:
            newBuffer.setData(buffer.asFloat());
            break;
        case HALF:
            // TODO
            throw new UnsupportedOperationException();
        case COMPRESSED:
            // TODO
            throw new UnsupportedOperationException();
        default:
            throw new UnsupportedOperationException();
    }
    return newBuffer;
}

Example 18 with DataBuffer

use of org.nd4j.linalg.api.buffer.DataBuffer in project nd4j by deeplearning4j.

the class BaseOp method extraArgsBuff.

@Override
public Buffer extraArgsBuff() {
    if (extraArgs != null) {
        DataBuffer retBuff;
        if (x.data().dataType() == DataBuffer.Type.FLOAT) {
            retBuff = Nd4j.createBuffer(new float[extraArgs.length]);
            for (int i = 0; i < extraArgs.length; i++) {
                Number val = (Number) extraArgs[i];
                retBuff.put(i, val.floatValue());
            }
            return retBuff.asNioFloat();
        } else {
            retBuff = Nd4j.createBuffer(new double[extraArgs.length]);
            for (int i = 0; i < extraArgs.length; i++) {
                Number val = (Number) extraArgs[i];
                retBuff.put(i, val.doubleValue());
            }
            return retBuff.asNioDouble();
        }
    }
    return null;
}

Example 19 with DataBuffer

use of org.nd4j.linalg.api.buffer.DataBuffer 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 20 with DataBuffer

use of org.nd4j.linalg.api.buffer.DataBuffer 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();
}