I have 2D array as:
Array(Array(1,1,0), Array(1,0,1))
and I would like to accumulate values over column so my final output look like
Array(Array(1,1,0), Array(2,1,1))
If this is 1D array, I can simply use 'scan' but I'm having trouble with using scan in 2D array.
can anyone help on this issue?
Here's one way to do it:
val t = Array(Array(1,1,0), Array(1,0,1))
val result = t.scanLeft(Array.fill(t(0).length)(0)) ((x,y) =>
x.zip(y).map(e => e._1 + e._2)).drop(1)
//to see the results
result.foreach(e => println(e.toList))
gives:
List(1, 1, 0)
List(2, 1, 1)
The idea is to create an array filled with zeros (using Array.fill) and then scan the 2D array using that as an accumulator. In the end, drop(1) gets rid of the zero-filled array.
EDIT:
In response to the comment, this solution works for a matrix of any size. The zip function takes care of element-wise addition.
EDIT 2 (Step by step explanation):
You already know about scan or a one-dimensional array. The idea is essentially the same.
We initialize the accumulator with zero. In this case, zero means an array of zeros. Array.fill is used to create an array filled with zeros.
Instead of a single addition, we need to add arrays element-wise. This is what the combination of zip and map do. There are a lot of examples available on the Internet about how these methods work.
Finally, we drop the zero element using Scala's drop(1). The result is an array of arrays containing accumulated values.
I would solve it as for given row r, sum all previous rows.
val accumatedMatrix =
for(row <- array.indices)
yield array.take(row + 1).foldLeft(Array(0, 0, 0)) {
case (a, b) => Array(a(0) + b(0), a(1) + b(1), a(2) + b(2))
}
input:
val array = Array(
Array(1,1,0),
Array(1,0,1)
)
output:
1,1,0
2,1,1
Instead of summing all the previous rows repeatedly you can improve it to memoize as well.
Pretty much the same approach as a 1D array:
a.tail.scan(a.head)((acc, value) =>
Array(acc(0) + value(0), acc(1) + value(1), acc(2) + value(2))
)
For arbitrary number (as long as they all have the same number):
a.tail.scan(a.head)((acc, value) =>
acc zip value map {case (a,b) => a+b}
)
Related
I'm a beginner in Scala and I'm trying to build a function that calculates the total sum of all the numbers contained in a matrix.
I tried to do this code :
val pixels = Vector(
Vector(0, 1, 2),
Vector(1, 2, 3)
)
def sum(matrix: Vector[Vector[Int]]): Int = {
matrix.reduce((a, b) => a + b)
}
println(sum(pixels))
But I get the following error: "value + is not a member of Vector[Int]"
I would like to have the sum total of all the numbers contained in the matrix as an integer.
Can you help me to solve this problem ?
Thank you in advance !
You defined matrix as a Vector of Vectors, so arguments to reduce are two Vectors, not ints. If you want to sum the all, you need to flatten first to pull the actual ints from the inner vectors. Also your function the way you wrote it does not return anything. You don't want that variable assignment:
def sum(matrix: Vector[Vector[Int]]) = matrix.flatten.reduce(_ + _)
I have a List in Scala:
val hdtList = hdt.split(",").toList
hdtList.foreach(println)
Output:
forecast_id bigint,period_year bigint,period_num bigint,period_name string,drm_org string,ledger_id bigint,currency_code string,source_system_name string,source_record_type string,gl_source_name string,gl_source_system_name string,year string,period string
There is an array which is obtained from a dataframe and converting its column to array as below:
val partition_columns = spColsDF.select("partition_columns").collect.flatMap(x => x.getAs[String](0).split(","))
partition_columns.foreach(println)
Output:
source_system_name
period_year
Is there a way to filter out the elements: source_system_name string, period_year bigint from hdtList by checking them against the elements in the Array: partition_columns and put them into new List.
I am confused on applying filter/map on the right collections appropriately and compare them.
Could anyone let me know how can I achieve that ?
Unless I'm misunderstanding the question, I think this is what you need:
val filtered = hdtList.filter { x =>
!partition_columns.exists { col => x.startsWith(col) }
}
In your case you need to use filter, because you need to remove elements from hdtList.
Map is a function that transform elements, there is no way to remove elements from a collection using map. If you have a List of X elements, after map execution, you have X elements, not less, not more.
val newList = hdtList.filter( x => partition_columns.exists(x.startsWith) )
Be aware that the combination filter+exists between two List is an algorithm NxM. If your Lists are big, you will have a performance problem.
One way to solve that problem is using Sets.
It might be useful to have both lists: the hdt elements referenced in partition_columns, and the hdt elements that aren't.
val (pc
,notPc) = hdtList.partition( w =>
partition_columns.contains(w.takeWhile(_!=' ')))
//pc: List[String] = List(period_year bigint, source_system_name string)
//notPc: List[String] = List(forecast_id bigint, period_num bigint, ... etc.
I am trying to write scala code which gives maximum sum from contiguous sub-array from the given array. For example, val arr= Array(-2, -3, 4, -1, -2, 1, 5, -3). In this array I need to get maximum contiguous sub-array sum i.e 4+(-1)+(-2)+(1)+5 = 7. I wrote following code for getting this result.
scala> arr.foldLeft(0) { (currsum,newnum) => if((currsum+newnum)<0) 0 else { if(currsum<(currsum+newnum)) (currsum+newnum) else currsum }}
res5: Int = 10
but deviated from actual result as I am unable to update the maximum_so_farvalue as the counting/summation goes on. Since I have used foldLeft to do this functionality, is there any possibility to update the maximum_so_farvariable only when sum of contiguous sub array elements is greater than previous max_sum?
reference link for better understanding of scenario
Well, obviously you have to propagate two values along your input data for this computation, just as you would need to do in the imperative case:
arr.foldLeft((0,0)){
case ((maxSum, curSum), value) => {
val newSum = Math.max(0, curSum + value)
(Math.max(maxSum, newSum), newSum)
}
}._1
An other way would be to compute the intermediate results (lazily if you want) and then select the maximum:
arr.toIterator.scanLeft(0){
case (curSum, value) =>
Math.max(0, curSum + value)
}.max
I have a list details of this type :
case class Detail(point: List[Double], cluster: Int)
val details = List(Detail(List(2.0, 10.0),1), Detail(List(2.0, 5.0),3),
Detail(List(8.0, 4.0),2), Detail(List(5.0, 8.0),2))
I want filter this list into a tuple which contains a sum of each corresponding point where the cluster is 2
So I filter this List :
details.filter(detail => detail.cluster == 2)
which returns :
List(Detail(List(8.0, 4.0),2), Detail(List(5.0, 8.0),2))
It's the summing of the corresponding values I'm having trouble with. In this example the tuple should contain (8+5, 4+8) = (13, 12)
I'm thinking to flatten the List and then sum each corresponding value but
List(details).flatten
just returns the same List
How to sum the corresponding values in the List into a Tuple ?
I could achieve this easily using a for loop and just extract the details I need into a counter but what is the functional solution ?
What do you want to happen if the lists for different Details have different lengths?
Or same length which is different from 2? Tuples are generally only used when you need a fixed in advance number of elements; you won't even be able to write a return type if you need tuples of different lengths.
Assuming that all of them are lists of the same length and you get a list in return, something like this should work (untested):
details.filter(_.cluster == 2).map(_.point).transpose.map(_.sum)
I.e. first get all points as a list of lists, transpose it so you get a list for each "coordinate", and sum each of these lists.
If you do know that each point has two coordinates, this should likely be reflected in your Point type, by using (Double, Double) instead of List[Double] and you can just fold over the list of points, which should be a bit more efficient. Look at definition of foldLeft and the standard implementation of sum in terms of foldLeft:
def sum(list: List[Int]): Int = list.foldLeft(0)((acc, x) => acc + x)
and it should be easy to do what you want.
You can use just one foldLeft with PF without filter:
details.foldLeft((0.0,0.0))({
case ((accX, accY), Detail(x :: y :: Nil, 2)) => (accX + x, accY + y)
case (acc, _) => acc
})
res1: (Double, Double) = (13.0,12.0)
I'm trying to understand the example here which computes Jaccard similarity between pairs of vectors in a matrix.
val aBinary = adjacencyMatrix.binarizeAs[Double]
// intersectMat holds the size of the intersection of row(a)_i n row (b)_j
val intersectMat = aBinary * aBinary.transpose
val aSumVct = aBinary.sumColVectors
val bSumVct = aBinary.sumRowVectors
//Using zip to repeat the row and column vectors values on the right hand
//for all non-zeroes on the left hand matrix
val xMat = intersectMat.zip(aSumVct).mapValues( pair => pair._2 )
val yMat = intersectMat.zip(bSumVct).mapValues( pair => pair._2 )
Why does the last comment mention non-zero values? As far as I'm aware, the ._2 function selects the second element of a pair independent of the first element. At what point are (0, x) pairs obliterated?
Yeah, I don't know anything about scalding but this seems odd. If you look at zip implementation it mentions specifically that it does an outer join to preserve zeros on either side. So it does not seem that the comment applies to how zeroes are actually treated in matrix.zip.
Besides looking at the dimension returned by zip, it really seems this line just replicates the aSumVct column vector for each column:
val xMat = intersectMat.zip(aSumVct).mapValues( pair => pair._2 )
Also I find the val bSumVct = aBinary.sumRowVectors suspicious, because it sums the matrix along the wrong dimension. It feels like something like this would be better:
val bSumVct = aBinary.tranpose.sumRowVectors
Which would conceptually be the same as aSumVct.transpose, so that at the end of the day, in the cell (i, j) of xMat + yMat we find the sum of elements of row(i) plus the sum of elements of row(j), then we subtract intersectMat to adjust for the double counting.
Edit: a little bit of googling unearthed this blog post: http://www.flavianv.me/post-15.htm. It seems the comments were related to that version where the vectors to compare are in two separate matrices that don't necessarily have the same size.