def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of vectors containing i.i.d. samples drawn
        from the uniform distribution U(0.0, 1.0).

        :param sc: SparkContext used to create the RDD.
        :param numRows: Number of Vectors in the RDD.
        :param numCols: Number of elements in each Vector.
        :param numPartitions: Number of partitions in the RDD.
        :param seed: Seed for the RNG that generates the seed for the generator in each partition.
        :return: RDD of Vector with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.

        >>> import numpy as np
        >>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())
        >>> mat.shape
        (10, 10)
        >>> mat.max() <= 1.0 and mat.min() >= 0.0
        True
        >>> RandomRDDs.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
        4
        """
        return callMLlibFunc("uniformVectorRDD", sc._jsc, numRows, numCols, numPartitions, seed)
Generates an RDD comprised of vectors containing i.i.d. samples drawn
        from the uniform distribution U(0.0, 1.0).

        :param sc: SparkContext used to create the RDD.
        :param numRows: Number of Vectors in the RDD.
        :param numCols: Number of elements in each Vector.
        :param numPartitions: Number of partitions in the RDD.
        :param seed: Seed for the RNG that generates the seed for the generator in each partition.
        :return: RDD of Vector with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.

        >>> import numpy as np
        >>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())
        >>> mat.shape
        (10, 10)
        >>> mat.max() <= 1.0 and mat.min() >= 0.0
        True
        >>> RandomRDDs.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
        4