def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of i.i.d. samples from the Gamma
        distribution with the input shape and scale.

        :param sc: SparkContext used to create the RDD.
        :param shape: shape (> 0) parameter for the Gamma distribution
        :param scale: scale (> 0) parameter for the Gamma distribution
        :param size: Size of the RDD.
        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
        :param seed: Random seed (default: a random long integer).
        :return: RDD of float comprised of i.i.d. samples ~ Gamma(shape, scale).

        >>> from math import sqrt
        >>> shape = 1.0
        >>> scale = 2.0
        >>> expMean = shape * scale
        >>> expStd = sqrt(shape * scale * scale)
        >>> x = RandomRDDs.gammaRDD(sc, shape, scale, 1000, seed=2)
        >>> stats = x.stats()
        >>> stats.count()
        1000
        >>> abs(stats.mean() - expMean) < 0.5
        True
        >>> abs(stats.stdev() - expStd) < 0.5
        True
        """
        return callMLlibFunc("gammaRDD", sc._jsc, float(shape),
                             float(scale), size, numPartitions, seed)
def gammaRDD ( sc , shape , scale , size , numPartitions = None , seed = None ) : return callMLlibFunc ( "gammaRDD" , sc . _jsc , float ( shape ) , float ( scale ) , size , numPartitions , seed )
Generates an RDD comprised of i.i.d. samples from the Gamma
        distribution with the input shape and scale.

        :param sc: SparkContext used to create the RDD.
        :param shape: shape (> 0) parameter for the Gamma distribution
        :param scale: scale (> 0) parameter for the Gamma distribution
        :param size: Size of the RDD.
        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
        :param seed: Random seed (default: a random long integer).
        :return: RDD of float comprised of i.i.d. samples ~ Gamma(shape, scale).

        >>> from math import sqrt
        >>> shape = 1.0
        >>> scale = 2.0
        >>> expMean = shape * scale
        >>> expStd = sqrt(shape * scale * scale)
        >>> x = RandomRDDs.gammaRDD(sc, shape, scale, 1000, seed=2)
        >>> stats = x.stats()
        >>> stats.count()
        1000
        >>> abs(stats.mean() - expMean) < 0.5
        True
        >>> abs(stats.stdev() - expStd) < 0.5
        True