Vector in MLlib

First, vectors come in two flavors: dense and sparse. Dense vectors store all their entries in an array of floating-point numbers.
In contrast, sparse vectors store only the nonzero values and their indices. Sparse vectors are usually preferable (both in terms of memory use and speed) if at most 10% of elements are nonzero.
MLlib’s Vector classes are primarily meant for data representation, but do not provide arithmetic operations such as addition and subtraction in the user API.

import org.apache.spark.mllib.linalg.Vectors

// Create the dense vector <1.0, 2.0, 3.0>; Vectors.dense takes values or an array
val denseVec2 = Vectors.dense(Array(1.0, 2.0, 3.0))

// Create the sparse vector <1.0, 0.0, 2.0, 0.0>; Vectors.sparse takes the size of
// the vector (here 4) and the positions and values of nonzero entries
val sparseVec1 = Vectors.sparse(4, Array(0, 2), Array(1.0, 2.0))