Singular value decomposition: Difference between revisions

=={{header|Julia}}==

Julia has an svd() function as part of its built-in LinearAlgebra package.

<syntaxhighlight lang="julia">

julia> using LinearAlgebra

julia> function testsvd()

rows, cols = [parse(Int, s) for s in split(readline())]

arr = zeros(rows, cols)

for row in 1:rows

arr[row, :] .= [tryparse(Float64, s) for s in split(readline())]

end

display(svd(arr))

end

testsvd (generic function with 1 method)

julia> testsvd()

2 2

3 0

4.5

SVD{Float64, Float64, Matrix{Float64}, Vector{Float64}}

U factor:

2×2 Matrix{Float64}:

-0.346946 -0.937885

-0.937885 0.346946

singular values:

2-element Vector{Float64}:

6.74492216626026

2.0015056760076915

Vt factor:

2×2 Matrix{Float64}:

-0.780042 -0.625727

-0.625727 0.780042

</syntaxhighlight>