I'm trying to create a 3D array that duplicates the elements (a copy of data) with rows remaining the same for both. This is ultimately to be a keras/tensorflow lstm input array (samples, timesteps, features) of a time series, where, for different reasons, I've determined that my timesteps are also my features and my samples are fixed (rows).
Some data:
perhaps <- matrix(sample(c(0:1), 20, replace = TRUE), nrow = 4,
ncol = 5)
print(perhaps)
perhaps
[,1] [,2] [,3] [,4] [,5]
[1,] 0 0 1 0 1
[2,] 0 0 0 0 1
[3,] 0 1 1 0 1
[4,] 1 0 0 1 1
perhaps_2_lst <- list(perhaps, perhaps)
all.equal(perhaps_2_lst[[1]], perhaps_2_lst[[2]])
[1] TRUE
#construct array from SOF inquestion:15213463
perhaps_arr <- array(
data = do.call(rbind, lapply(perhaps_2_lst, as.vector)),
dim = c(dim = c(dim(perhaps_2_lst[[1]])[1],
dim(perhaps_2_lst[[1]])[2], dim(perhaps_2_lst[[1]])[2]))
dim(perhaps_arr)
[1] 4 5 5
Encouraging.
print(perhaps_arr)
, , 1
[,1] [,2] [,3] [,4] [,5]
[1,] 0 0 0 1 1
[2,] 0 0 0 1 1
[3,] 0 1 0 0 0
[4,] 0 1 0 0 0
, , 2
[,1] [,2] [,3] [,4] [,5]
[1,] 1 0 0 1 1
[2,] 1 0 0 1 1
[3,] 0 0 1 1 1
[4,] 0 0 1 1 1
, , 3
[,1] [,2] [,3] [,4] [,5]
[1,] 0 0 0 1 1
[2,] 0 0 0 1 1
[3,] 0 1 0 0 0
[4,] 0 1 0 0 0
, , 4
[,1] [,2] [,3] [,4] [,5]
[1,] 1 0 0 1 1
[2,] 1 0 0 1 1
[3,] 0 0 1 1 1
[4,] 0 0 1 1 1
, , 5
[,1] [,2] [,3] [,4] [,5]
[1,] 0 0 0 1 1
[2,] 0 0 0 1 1
[3,] 0 1 0 0 0
[4,] 0 1 0 0 0
Opps. Not that familiar with arrays, I guess. I was expecting something like:
, , 1
#perhaps
[,1] [,2] [,3] [,4] [,5]
[1,] 0 0 1 0 1
[2,] 0 0 0 0 1
[3,] 0 1 1 0 1
[4,] 1 0 0 1 1
, , 2
#perhaps
[,1] [,2] [,3] [,4] [,5]
[1,] 0 0 1 0 1
[2,] 0 0 0 0 1
[3,] 0 1 1 0 1
[4,] 1 0 0 1 1
ie. same data repeated. And now stumped. Suggestions and clarification towards understanding what's happening here greatly appreciated.