I have the number of columns equals the number of rows. And the the diagonal is is equal to zero. How can I build this matrix?
#mat
# [,1] [,2] [,3] [,4]
#[1,] 0 NA NA NA
#[2,] 1 0 NA NA
#[3,] 2 4 0 NA
#[4,] 3 5 6 0
I tried this
x=rand(4,4)
4x4 Array{Float64,2}:
0.60064 0.917443 0.561744 0.135717
0.106728 0.72391 0.0894174 0.0656103
0.410262 0.953857 0.844697 0.0375045
0.476771 0.778106 0.469514 0.398846
c=LowerTriangular(x)
4x4 LowerTriangular{Float64,Array{Float64,2}}:
0.60064 0.0 0.0 0.0
0.106728 0.72391 0.0 0.0
0.410262 0.953857 0.844697 0.0
0.476771 0.778106 0.469514 0.398846
but l'm looking for something like this
c=LowerTriangular(x)
4x4 LowerTriangular{Float64,Array{Float64,2}}:
0.0 NA NA NA
0.106728 0.0 NA NA
0.410262 0.953857 0.0 NA
0.476771 0.778106 0.469514 0
The diagonal should be equal to zero.
Matrices in Julia are the heterogeneous type of containers and hence, they can hold elements of any data type. It is not mandatory to define the data type of a matrix before assigning the elements to the matrix. Julia automatically decides the data type of the matrix by analyzing the values assigned to it.
In Julia we can concatenate a matrix to another matrix to the right side of the initial matrix or to the bottom of it. We use vcat (A, B) to concatenate to the side. And hcat (A, B) to concatenate to the bottom.
Linear algebra functions in Julia are largely implemented by calling functions from LAPACK. Sparse matrix factorizations call functions from SuiteSparse. Other sparse solvers are available as Julia packages. Base.:* — Method Matrix multiplication. Base.:\ — Method Matrix division using a polyalgorithm.
The transpose operation flips the matrix over its diagonal by switching the rows and columns. Let A be a matrix. We can get the transpose of A by using A’. A matrix in Julia can be flipped via the X-axis i.e. horizontally or via the Y-axis i.e. vertically.
You might want to use a list comprehension. But it would be good if you could give more information in the question about what you are trying to do.
numrows =4
numcols = 4
[ x>y ? 1 : (x == y ? 0 : NaN) for x in 1:numrows, y in 1:numcols]
which will give:
0 NaN NaN NaN
1 0 NaN NaN
1 1 0 NaN
1 1 1 0
For any number of rows and columns. And then you can work from there.
See docs for list comprehensions & conditionals:
http://docs.julialang.org/en/release-0.4/manual/arrays/#comprehensions
http://docs.julialang.org/en/release-0.4/manual/control-flow/#man-conditional-evaluation
Here is something taking inspiration from Code by Stefan Karpinski on the Julia User's list:
function vec2ltri_alt{T}(v::AbstractVector{T}, z::T=zero(T))
n = length(v)
v1 = vcat(0,v)
s = round(Int,(sqrt(8n+1)-1)/2)
s*(s+1)/2 == n || error("vec2utri: length of vector is not triangular")
s+=1
[ i>j ? v1[round(Int, j*(j-1)/2+i)] : (i == j ? z : NaN) for i=1:s, j=1:s ]
end
julia> vec2ltri_alt(collect(1:6))
4x4 Array{Any,2}:
0 NaN NaN NaN
1 0 NaN NaN
2 3 0 NaN
3 4 6 0
Note: If desired, check out the official documentation on the ternary operator for a bit more clarity on what is going on with the ? ... :
syntax here.
For those looking for a more "standard" diagonal matrix solution:
Here is a version that creates a more standard solution:
function vec2ltri{T}(v::AbstractVector{T}, z::T=zero(T))
n = length(v)
s = round(Int,(sqrt(8n+1)-1)/2)
s*(s+1)/2 == n || error("vec2utri: length of vector is not triangular")
[ i>=j ? v[round(Int, j*(j-1)/2+i)] : z for i=1:s, j=1:s ]
end
a = vec2ltri(collect(1:6))
julia> a = vec2ltri(collect(1:6))
3x3 Array{Int64,2}:
1 0 0
2 3 0
3 4 6
julia> istril(a) ## verify matrix is lower triangular
true
If you want upper triangular: instead of lower, just change the i<=j
to i>=j
.
Other random tools Note also functions like tril!(a)
which will convert in place a given matrix to lower triangular, replacing everything above the main diagonal with zeros. See the Julia documentation for more info on this function, as well as various other related tools.
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