pyspark/bigdl/util/common.py
def to_sample_rdd(x, y, numSlices=None):
    """
    Conver x and y into RDD[Sample]
    :param x: ndarray and the first dimension should be batch
    :param y: ndarray and the first dimension should be batch
    :param numSlices:
    :return:
    """
    sc = get_spark_context()
    from bigdl.util.common import Sample
    x_rdd = sc.parallelize(x, numSlices)
    y_rdd = sc.parallelize(y, numSlices)
    return x_rdd.zip(y_rdd).map(lambda item: Sample.from_ndarray(item[0], item[1]))
def to_sample_rdd(x, y, numSlices=None):
    """
    Conver x and y into RDD[Sample]
    :param x: ndarray and the first dimension should be batch
    :param y: ndarray and the first dimension should be batch
    :param numSlices:
    :return:
    """
    sc = get_spark_context()
    from bigdl.util.common import Sample
    x_rdd = sc.parallelize(x, numSlices)
    y_rdd = sc.parallelize(y, numSlices)
    return x_rdd.zip(y_rdd).map(lambda item: Sample.from_ndarray(item[0], item[1]))
def to_sample_rdd ( x , y , numSlices = None ) : sc = get_spark_context ( ) from bigdl . util . common import Sample x_rdd = sc . parallelize ( x , numSlices ) y_rdd = sc . parallelize ( y , numSlices ) return x_rdd . zip ( y_rdd ) . map ( lambda item : Sample . from_ndarray ( item [ 0 ] , item [ 1 ] ) )
Conver x and y into RDD[Sample]
    :param x: ndarray and the first dimension should be batch
    :param y: ndarray and the first dimension should be batch
    :param numSlices:
    :return:
Conver x and y into RDD [ Sample ] : param x : ndarray and the first dimension should be batch : param y : ndarray and the first dimension should be batch : param numSlices : : return :