mwsumTopN
Syntax
mwsumTopN(X, Y, S, window, top, [ascending=true], [tiesMethod=’oldest’])
Please see Moving TopN Functions (mTopN functions) for the parameters and windowing logic.
Details
After stably sorting S in the specified ascending order, the function obtains the first top pairs of elements in X and Y in the sliding window and calculates the cumulative weighted sum of X with Y as the weights.
Examples
$ x = NULL 3 8 4 0 7 4
$ y = 2 3 1 7 3 6 1
$ s = 5 NULL 8 9 9 4 4
$ mwsumTopN(x, y, s, 4, 3)
[,,8,36,36,78,74]
$ s2=2021.01.01 2021.02.03 2021.01.23 2021.04.06 2021.12.29 2021.04.16 2021.10.29
$ mwsumTopN(x, y, s2, 3, 2)
[ , 9, 8, 17, 36, 70, 46]
$ x1 = matrix(x, 4 3 6 2 3 1 3)
$ y1=matrix(3 7 9 3 2 4 6, y)
$ s1=matrix(2 3 1 7 3 NULL 1, s)
$ mwsumTopN(x1,y1,s1,4,3)
#1 |
#2 |
---|---|
8 |
|
21 |
8 |
93 |
14 |
93 |
28 |
93 |
29 |
84 |
26 |
36 |
23 |
$ mwsumTopN(x1, y1, s, 4, 3)
#1 |
#2 |
---|---|
8 |
|
8 |
|
72 |
14 |
84 |
28 |
84 |
29 |
112 |
26 |
64 |
23 |
$ n = 3000
$ ids = 1..3000
$ dates = take(2021.01.01..2021.10.01,n)
$ prices = rand(1000,n)
$ vals = rand(1000,n)
$ t = table(ids as id,dates as date,prices as price,vals as val)
$ dbName = "dfs://test_mwsumTopN_2"
$ if(existsDatabase(dbName))dropDB(dbName)
$ db = database(dbName,VALUE,1..5000)
$ pt = db.createPartitionedTable(t,"pt",`id).append!(t)
$ select mwsumTopN(price, val, id, 10, 5, true) from pt where date>2021.05.01
Related function: mwsum