SymmetricTensors.jl

SymmetricTensors.jl provides the SymmetricTensors{T, N} type used to store fully symmetric tensors in more efficient way, without most of redundant repetitions. It uses blocks of Array{T, N} stored in Union{Array{Float,N}, Nothing} structure. Repeated blocks are replaced by Void. The module introduces SymmetricTensors{T, N} type and some basic operations on this type. As of 01/01/2017 @kdomino is the lead maintainer of this package.

Installation

Within Julia, just use run

pkg> add SymmetricTensors

to install the files. Julia 1.0 or later is required.

Constructor

julia> data = ones(4,4);


julia> SymmetricTensor{Float64,2}(Union{Nothing, Array{Float64,2}}[[1.0 1.0; 1.0 1.0] [1.0 1.0; 1.0 1.0]; nothing [1.0 1.0; 1.0 1.0]], 2, 2, 4, true)

Converting

From Array{T, N} to SymmetricTensors{T, N}

julia> SymmetricTensors(data::Array{T, N}, bls::Int = 2)

where bls is the size of a block

julia> data = ones(4,4);


julia> convert(SymmetricTensor, data, 2)
SymmetricTensor{Float64,2}(Union{Nothing, Array{Float64,2}}[[1.0 1.0; 1.0 1.0] [1.0 1.0; 1.0 1.0]; nothing [1.0 1.0; 1.0 1.0]], 2, 2, 4, true)

From SymmetricTensors{T, N} to Array{T, N}

julia> Array(st::SymmetricTensors{T, N})

Fields

frame::ArrayNArrays{T,N}: stores data, where ArrayNArrays{T,N} = Array{Union{Array{T, N}, Nothing}, N}
bls::Int: size of a block,
bln::Int: number of blocks,
dats::Int: size of data,
sqr::Bool: shows if the last block is squared.

Operations

Elementwise addition: +, - is supported between many SymmetricTensors{T, N} while elementwise substraction: - between two SymmetricTensors{T, N}. Addition substraction multiplication and division +, -, *, / is supported between SymmetricTensors{T, N} and a number.

julia> x = SymmetricTensor(ones(4,4));

julia> y = SymmetricTensor(2*ones(4,4));

julia> x+y
SymmetricTensor{Float64,2}(Union{Nothing, Array{Float64,2}}[[3.0 3.0; 3.0 3.0] [3.0 3.0; 3.0 3.0]; #undef [3.0 3.0; 3.0 3.0]], 2, 2, 4, true)

julia> x*10
SymmetricTensor{Float64,2}(Union{Nothing, Array{Float64,2}}[[10.0 10.0; 10.0 10.0] [10.0 10.0; 10.0 10.0]; #undef [10.0 10.0; 10.0 10.0]], 2, 2, 4, true)

The function diag returns a Vector{T}, of all super-diagonal elements of a SymmetricTensor.

julia> data = ones(5,5,5,5);

julia> st = SymmetricTensor(data);

julia> diag(st)
5-element Array{Float64,1}:
 1.0
 1.0
 1.0
 1.0
 1.0

Random Symmetric Tensor generation

To generate random Symmetric Tensor with random elements of typer T form a uniform distribution on [0,1) use rand(SymmetricTensor{T, N}, n::Int, b::Int = 2). Here n denotes data size and b denotes block size.

julia> using Random

julia> Random.seed!(42)

julia> rand(SymmetricTensor{Float64, 2}, 2)
SymmetricTensor{Float64,2}(Union{Nothing, Array{Float64,2}}[[0.533183 0.454029; 0.454029 0.0176868]], 2, 1, 2, true)

getindex and setindex!

julia> using Random

julia> Random.seed!(42)

julia> st = rand(SymmetricTensor{Float64, 2}, 2)
SymmetricTensor{Float64,2}(Union{Nothing, Array{Float64,2}}[[0.533183 0.454029; 0.454029 0.0176868]], 2, 1, 2, true)

julia> st[1,2]
0.4540291355871424

julia> st[2,1]
0.4540291355871424

setindex!(st::SymmetricTensor, x::Float, mulind::Int...) changes all symmetric tensor's elements indexed by mulind to x.

julia> st[1,2] = 10.

julia> convert(Array, st)
2×2 Array{Float64,2}:
  0.533183  10.0      
 10.0        0.0176868

Auxiliary function

julia> unfold(data::Array{T,N}, mode::Int)

unfolds data in a given mode

julia> a = reshape(collect(1.:8.), (2,2,2))
2×2×2 Array{Float64,3}:
[:, :, 1] =
 1.0  3.0
 2.0  4.0

[:, :, 2] =
 5.0  7.0
 6.0  8.0

julia> unfold(a, 1)
2×4 Array{Float64,2}:
 1.0  3.0  5.0  7.0
 2.0  4.0  6.0  8.0

julia> unfold(a, 2)
2×4 Array{Float64,2}:
 1.0  2.0  5.0  6.0
 3.0  4.0  7.0  8.0

julia> unfold(a, 3)
2×4 Array{Float64,2}:
 1.0  2.0  3.0  4.0
 5.0  6.0  7.0  8.0

This project was partially financed by the National Science Centre, Poland – project number 2014/15/B/ST6/05204.

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