Adding Sparse Vectors 3.0.0 Apache Spark Scala - scala

Question

I am trying to create a function as the following to add
two org.apache.spark.ml.linalg.Vector. or i.e two sparse vectors
This vector could look as the following
(28,[1,2,3,4,7,11,12,13,14,15,17,20,22,23,24,25],[0.13028398104008743,0.23648605632753023,0.7094581689825907,0.13028398104008743,0.23648605632753023,0.0,0.14218861229025295,0.3580566057240087,0.14218861229025295,0.13028398104008743,0.26056796208017485,0.0,0.14218861229025295,0.06514199052004371,0.13028398104008743,0.23648605632753023])
For e.g.
def add_vectors(x: org.apache.spark.ml.linalg.Vector,y:org.apache.spark.ml.linalg.Vector): org.apache.spark.ml.linalg.Vector = {
}
Let's look at a use case
val x = Vectors.sparse(2, List(0), List(1)) // [1, 0]
val y = Vectors.sparse(2, List(1), List(1)) // [0, 1]
I want to output to be
Vectors.sparse(2, List(0,1), List(1,1))
Here's another case where they share the same indices
val x = Vectors.sparse(2, List(1), List(1))
val y = Vectors.sparse(2, List(1), List(1))
This output should be
Vectors.sparse(2, List(1), List(2))
I've realized doing this is harder than it seems. I looked into one possible solution of converting the vectors into breeze, adding them in breeze and then converting it back to a vector. e.g Addition of two RDD[mllib.linalg.Vector]'s. So I tried implementing this.
def add_vectors(x: org.apache.spark.ml.linalg.Vector,y:org.apache.spark.ml.linalg.Vector) ={
val dense_x = x.toDense
val dense_y = y.toDense
val bv1 = new DenseVector(dense_x.toArray)
val bv2 = new DenseVector(dense_y.toArray)
val vectout = Vectors.dense((bv1 + bv2).toArray)
vectout
}
however this gave me an error in the last line
val vectout = Vectors.dense((bv1 + bv2).toArray)
Cannot resolve the overloaded method 'dense'.
I'm wondering why is error is occurring and ways to fix it?

Answer 1

To answer my own question, I had to think about how sparse vectors are. For e.g. Sparse Vectors require 3 arguments. the number of dimensions, an array of indices, and finally an array of values. For e.g.
val indices: Array[Int] = Array(1,2)
val norms: Array[Double] = Array(0.5,0.3)
val num_int = 4
val vector: Vector = Vectors.sparse(num_int, indices, norms)
If I converted this SparseVector to an Array I would get the following.
code:
val choiced_array = vector.toArray
choiced_array.map(element => print(element + " "))
Output:
[0.0, 0.5,0.3,0.0].
This is considered a more dense representation of it. So once you convert the two vectors to array you can add them with the following code
val add: Array[Double] = (vector.toArray, vector_2.toArray).zipped.map(_ + _)
This gives you another array of them both added. Next to create your new sparse vector, you would want to create an indices array as shown in the construction
var i = -1;
val new_indices_pre = add.map( (element:Double) => {
i = i + 1
if(element > 0.0)
i
else{
-1
}
})
Then lets filter out all -1 indices indication that indicate zero for that indice.
new_indices_pre.filter(element => element != -1)
Remember to filter out none zero values from the array which has the addition of the two vectors.
val final_add = add.filter(element => element > 0.0)
Lastly, we can make the new sparse Vector
Vectors.sparse(num_int,new_indices,final_add)