Reshaping a circular buffer to the thrid dimension

user_417896 0
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We can reshape a regular matrix to the third dimension using:

julia> data = rand(4,2)
4×2 Array{Float64,2}:
 0.89585   0.328315
 0.77878   0.619666
 0.232389  0.132091
 0.48543   0.829476

julia> reshape(data, 4, 1, 2)
4×1×2 Array{Float64,3}:
[:, :, 1] =
 0.895850499952602
 0.7787804133322247
 0.23238945917674037
 0.4854297310447009

[:, :, 2] =
 0.3283154491436233
 0.6196660556881552
 0.13209084702809903
 0.8294762758800456

But if using CircularBuffer we get an error:

using DataStructures

julia> data = CircularBuffer{Vector{Float64}}(4)
0-element CircularBuffer{Array{Float64,1}}

julia> push!(data, rand(2))
1-element CircularBuffer{Array{Float64,1}}:
 [0.0271144, 0.131345]

julia> push!(data, rand(2))
2-element CircularBuffer{Array{Float64,1}}:
 [0.0271144, 0.131345]
 [0.0483998, 0.384114]

julia> push!(data, rand(2))
3-element CircularBuffer{Array{Float64,1}}:
 [0.0271144, 0.131345]
 [0.0483998, 0.384114]
 [0.856657, 0.239313]

julia> push!(data, rand(2))
4-element CircularBuffer{Array{Float64,1}}:
 [0.0271144, 0.131345]
 [0.0483998, 0.384114]
 [0.856657, 0.239313]
 [0.573953, 0.0423042]

julia> reshape(data, 4, 1, 2)
ERROR: DimensionMismatch("parent has 4 elements, which is incompatible with size (4, 1, 2)")
Stacktrace:
 [1] _throw_dmrs(::Int64, ::String, ::Tuple{Int64,Int64,Int64}) at ./reshapedarray.jl:180
 [2] _reshape at ./reshapedarray.jl:175 [inlined]
 [3] reshape(::CircularBuffer{Array{Float64,1}}, ::Tuple{Int64,Int64,Int64}) at ./reshapedarray.jl:112
 [4] reshape(::CircularBuffer{Array{Float64,1}}, ::Int64, ::Int64, ::Vararg{Int64,N} where N) at ./reshapedarray.jl:115
 [5] top-level scope at none:0
julia
2 Answers

Your CirclularBuffer is a Vector of size 4x1 so it has 4 elements. Hence you need a reshaped structure with that element count:

julia> CircularBuffer <: AbstractVector
true

julia> reshape(data,2,1,2)
2×1×2 reshape(::CircularBuffer{Array{Float64,1}}, 2, 1, 2) with eltype Array{Float64,1}:
[:, :, 1] =
 [0.8553997132170639, 0.823601762586583]
 [0.16126832224468735, 0.11158333622955818]

[:, :, 2] =
 [0.3559049470946831, 0.8110608687196386]
 [0.7729569142023647, 0.9370118594277839]

Another option is to have a CircularBuffer 8x1 and then reshape it - see the Julia session below:

julia> data2 = CircularBuffer{Float64}(8);

julia> push!.(Ref(data2), rand(8));

julia> reshape(data2, 4, 1, 2)
4×1×2 reshape(::CircularBuffer{Float64}, 4, 1, 2) with eltype Float64:
[:, :, 1] =
 0.016467979685045098
 0.14558440901833336
 0.11701214175525476
 0.7006868771470229

[:, :, 2] =
 0.03545592972243128
 0.19139585572379736
 0.5295403356035531
 0.5409464215739019
user_99577100

I guess what you want is a CircularArrayBuffer

julia> using ReinforcementLearningCore

julia> b = CircularArrayBuffer{Float64}(4, 2)
4×0 CircularArrayBuffer{Float64,2}

julia> push!(b, rand(4));

julia> push!(b, rand(4));

julia> reshape(b, 4, 1, 2)
4×1×2 reshape(::CircularArrayBuffer{Float64,2}, 4, 1, 2) with eltype Float64:
[:, :, 1] =
 0.09621058339946265
 0.19652636521722577
 0.14816367263437913
 0.5415815617368629

[:, :, 2] =
 0.38976815167466494
 0.9344752986999203
 0.43187275186834295
 0.7951761882018082
user_34013120
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