Wrong comment in random matrix generator in spark-als algorithm

private def initialize[ID](
    inBlocks: RDD[(Int, InBlock[ID])],
    rank: Int,
    seed: Long): RDD[(Int, FactorBlock)] = {
  // Choose a unit vector uniformly at random from the unit sphere, but from the
  // "first quadrant" where all elements are nonnegative. This can be done by choosing
  // elements distributed as Normal(0,1) and taking the absolute value, and then normalizing.
  // This appears to create factorizations that have a slightly better reconstruction
  // (<1%) compared picking elements uniformly at random in [0,1].
  inBlocks.mapPartitions({ iter =>
    iter.map {
      case (srcBlockId, inBlock) =>
        val random: XORShiftRandom = new XORShiftRandom(byteswap64(seed ^ srcBlockId))
        val factors: Array[Array[Float]] = Array.fill(inBlock.srcIds.length) {
          val factor = Array.fill(rank)(random.nextGaussian().toFloat)
          val nrm: Float = blas.snrm2(rank, factor, 1)
          blas.sscal(rank, 1.0f / nrm, factor, 1)
          factor
        }
        (srcBlockId, factors)
    }
  }, preservesPartitioning = true)
} 

/** 
* @return the next pseudorandom, Gaussian ("normally") distributed
 *         {@code double} value with mean {@code 0.0} and
 *         standard deviation {@code 1.0} from this random number
 *         generator's sequence
 */
synchronized public double nextGaussian() {
    // See Knuth, ACP, Section 3.4.1 Algorithm C.
    if (haveNextNextGaussian) {
        haveNextNextGaussian = false;
        return nextNextGaussian;
    } else {
        double v1, v2, s;
        do {
            v1 = 2 * nextDouble() - 1; // between -1 and 1
            v2 = 2 * nextDouble() - 1; // between -1 and 1
            s = v1 * v1 + v2 * v2;
        } while (s >= 1 || s == 0);
        double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s);
        nextNextGaussian = v2 * multiplier;
        haveNextNextGaussian = true;
        return v1 * multiplier;
    }
}