Generates an RDD comprised of i.i.d. samples from the log normal
        distribution with the input mean and standard distribution.

        :param sc: SparkContext used to create the RDD.
        :param mean: mean for the log Normal distribution
        :param std: std for the log Normal 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 ~ log N(mean, std).

        >>> from math import sqrt, exp
        >>> mean = 0.0
        >>> std = 1.0
        >>> expMean = exp(mean + 0.5 * std * std)
        >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std))
        >>> x = RandomRDDs.logNormalRDD(sc, mean, std, 1000, seed=2)
        >>> stats = x.stats()
        >>> stats.count()
        1000
        >>> abs(stats.mean() - expMean) < 0.5
        True
        >>> from math import sqrt
        >>> abs(stats.stdev() - expStd) < 0.5
        True

def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of i.i.d. samples from the log normal
        distribution with the input mean and standard distribution.

        :param sc: SparkContext used to create the RDD.
        :param mean: mean for the log Normal distribution
        :param std: std for the log Normal 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 ~ log N(mean, std).

        >>> from math import sqrt, exp
        >>> mean = 0.0
        >>> std = 1.0
        >>> expMean = exp(mean + 0.5 * std * std)
        >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std))
        >>> x = RandomRDDs.logNormalRDD(sc, mean, std, 1000, seed=2)
        >>> stats = x.stats()
        >>> stats.count()
        1000
        >>> abs(stats.mean() - expMean) < 0.5
        True
        >>> from math import sqrt
        >>> abs(stats.stdev() - expStd) < 0.5
        True
        """
        return callMLlibFunc("logNormalRDD", sc._jsc, float(mean), float(std),
                             size, numPartitions, seed)