Generates an RDD comprised of vectors containing i.i.d. samples drawn
        from the log normal distribution.

        :param sc: SparkContext used to create the RDD.
        :param mean: Mean of the log normal distribution
        :param std: Standard Deviation of the log normal distribution
        :param numRows: Number of Vectors in the RDD.
        :param numCols: Number of elements in each Vector.
        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
        :param seed: Random seed (default: a random long integer).
        :return: RDD of Vector with vectors containing i.i.d. samples ~ log `N(mean, std)`.

        >>> import numpy as np
        >>> 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))
        >>> m = RandomRDDs.logNormalVectorRDD(sc, mean, std, 100, 100, seed=1).collect()
        >>> mat = np.matrix(m)
        >>> mat.shape
        (100, 100)
        >>> abs(mat.mean() - expMean) < 0.1
        True
        >>> abs(mat.std() - expStd) < 0.1
        True

def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of vectors containing i.i.d. samples drawn
        from the log normal distribution.

        :param sc: SparkContext used to create the RDD.
        :param mean: Mean of the log normal distribution
        :param std: Standard Deviation of the log normal distribution
        :param numRows: Number of Vectors in the RDD.
        :param numCols: Number of elements in each Vector.
        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
        :param seed: Random seed (default: a random long integer).
        :return: RDD of Vector with vectors containing i.i.d. samples ~ log `N(mean, std)`.

        >>> import numpy as np
        >>> 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))
        >>> m = RandomRDDs.logNormalVectorRDD(sc, mean, std, 100, 100, seed=1).collect()
        >>> mat = np.matrix(m)
        >>> mat.shape
        (100, 100)
        >>> abs(mat.mean() - expMean) < 0.1
        True
        >>> abs(mat.std() - expStd) < 0.1
        True
        """
        return callMLlibFunc("logNormalVectorRDD", sc._jsc, float(mean), float(std),
                             numRows, numCols, numPartitions, seed)