I can never remember how to do this this.
How can go
from a Vector (size (n1)) to a Column Matrix (size (n1,1))?
or from a Matrix (size (n1,n2)) to a Array{T,3} (size (n1,n2,1))?
or from a Array{T,3} (size (n1,n2,n3)) to a Array{T,4} (size (n1,n2,n3, 1))?
and so forth.
I want to know to take Array and use it to define a new Array with an extra singleton trailing dimension.
I.e. the opposite of squeeze
You can do this with reshape.
You could define a method for this:
add_dim(x::Array) = reshape(x, (size(x)...,1))
julia> add_dim([3;4])
2×1 Array{Int64,2}:
3
4
julia> add_dim([3;4])
2×1 Array{Int64,2}:
3
4
julia> add_dim([3 30;4 40])
2×2×1 Array{Int64,3}:
[:, :, 1] =
3 30
4 40
julia> add_dim(rand(4,3,2))
4×3×2×1 Array{Float64,4}:
[:, :, 1, 1] =
0.483307 0.826342 0.570934
0.134225 0.596728 0.332433
0.597895 0.298937 0.897801
0.926638 0.0872589 0.454238
[:, :, 2, 1] =
0.531954 0.239571 0.381628
0.589884 0.666565 0.676586
0.842381 0.474274 0.366049
0.409838 0.567561 0.509187
Another easy way other than reshaping to an exact shape, is to use cat and ndims together. This has the added benefit that you can specify "how many extra (singleton) dimensions you would like to add". e.g.
a = [1 2 3; 2 3 4];
cat(ndims(a) + 0, a) # add zero singleton dimensions (i.e. stays the same)
cat(ndims(a) + 1, a) # add one singleton dimension
cat(ndims(a) + 2, a) # add two singleton dimensions
etc.
UPDATE (julia 1.3). The syntax for cat has changed in julia 1.3 from cat(dims, A...) to cat(A...; dims=dims).
Therefore the above example would become:
a = [1 2 3; 2 3 4];
cat(a; dims = ndims(a) + 0 )
cat(a; dims = ndims(a) + 1 )
cat(a; dims = ndims(a) + 2 )
etc.
Obviously, like Dan points out below, this has the advantage that it's nice and clean, but it comes at the cost of allocation, so if speed is your top priority and you know what you're doing, then in-place reshape operations will be faster and are to be preferred.
Some time before the Julia 1.0 release a reshape(x, Val{N}) overload was added which for N > ndim(x) results in the adding of right most singleton dimensions.
So the following works:
julia> add_dim(x::Array{T, N}) where {T,N} = reshape(x, Val(N+1))
add_dim (generic function with 1 method)
julia> add_dim([3;4])
2×1 Array{Int64,2}:
3
4
julia> add_dim([3 30;4 40])
2×2×1 Array{Int64,3}:
[:, :, 1] =
3 30
4 40
julia> add_dim(rand(4,3,2))
4×3×2×1 Array{Float64,4}:
[:, :, 1, 1] =
0.0737563 0.224937 0.6996
0.523615 0.181508 0.903252
0.224004 0.583018 0.400629
0.882174 0.30746 0.176758
[:, :, 2, 1] =
0.694545 0.164272 0.537413
0.221654 0.202876 0.219014
0.418148 0.0637024 0.951688
0.254818 0.624516 0.935076
Try this
function extend_dims(A,which_dim)
s = [size(A)...]
insert!(s,which_dim,1)
return reshape(A, s...)
end
the variable extend_dim specifies which dimension to extend
Thus
extend_dims(randn(3,3),1)
will produce a 1 x 3 x 3 array and so on.
I find this utility helpful when passing data into convolutional neural networks.
Related
I am using Julia1.6
Here, X is a D-order multidimeonal array.
How can I slice from i to j on the d-th axis of X ?
Here is an exapmle in case of D=6 and d=4.
X = rand(3,5,6,6,5,6)
Y = X[:,:,:,i:j,:,:]
i and j are given values which are smaller than 6 in the above example.
You can use the built-in function selectdim
help?> selectdim
search: selectdim
selectdim(A, d::Integer, i)
Return a view of all the data of A where the index for dimension d equals i.
Equivalent to view(A,:,:,...,i,:,:,...) where i is in position d.
Examples
≡≡≡≡≡≡≡≡≡≡
julia> A = [1 2 3 4; 5 6 7 8]
2×4 Matrix{Int64}:
1 2 3 4
5 6 7 8
julia> selectdim(A, 2, 3)
2-element view(::Matrix{Int64}, :, 3) with eltype Int64:
3
7
Which would be used something like:
julia> a = rand(10,10,10,10);
julia> selectedaxis = 5
5
julia> indices = 1:2
1:2
julia> selectdim(a,selectedaxis,indices)
Notice that in the documentation example, i is an integer, but you can use ranges of the form i:j as well.
If you need to just slice on a single axis, use the built in selectdim(A, dim, index), e.g., selectdim(X, 4, i:j).
If you need to slice more than one axis at a time, you can build the array that indexes the array by first creating an array of all Colons and then filling in the specified dimensions with the specified indices.
function selectdims(A, dims, indices)
indexer = repeat(Any[:], ndims(A))
for (dim, index) in zip(dims, indices)
indexer[dim] = index
end
return A[indexer...]
end
idx = ntuple( l -> l==d ? (i:j) : (:), D)
Y = X[idx...]
Let's assume I have two vectors x = [1, 2] and y = [3, 4]. How to best combine them to get a matrix m = [1 2; 3 4] in Julia Programming language? Thanks in advance for your support.
Note that in vcat(x', y') the operation x' is adjoint so it should not be used if you are working with complex numbers or vector elements that do not have adjoint defined (e.g. strings). Therefore then permutedims should be used but it will be slower as it allocates. A third way to do it is (admittedly it is more cumbersome to type):
julia> [reshape(x, 1, :); reshape(y, 1, :)]
2×2 Array{Int64,2}:
1 2
3 4
It is non allocating like [x'; y'] but does not do a recursive adjoint.
EDIT:
Note for Cameron:
julia> x = repeat(string.('a':'z'), 10^6);
julia> #btime $x';
1.199 ns (0 allocations: 0 bytes)
julia> #btime reshape($x, 1, :);
36.455 ns (2 allocations: 96 bytes)
so reshape allocates but only minimally (it needs to create an array object, while x' creates an immutable struct which does not require allocation).
Also I think it was a design decision to allocate. As for isbitsunion types actually reshape returns a struct so it does not allocate (similarly like for ranges):
julia> #btime reshape($x, 1, :)
12.211 ns (0 allocations: 0 bytes)
1×2 reshape(::Array{Union{Missing, Int64},1}, 1, 2) with eltype Union{Missing, Int64}:
1 missing
Two ways I know of:
julia> x = [1,2];
julia> y = [3,4];
julia> vcat(x', y')
2×2 Array{Int64,2}:
1 2
3 4
julia> permutedims(hcat(x, y))
2×2 Array{Int64,2}:
1 2
3 4
One more option - this one works both with numbers and other objects as Strings:
julia> rotl90([y x])
2×2 Array{Int64,2}:
1 2
3 4
What about
vcat(transpose(x), transpose(y))
or
[transpose(x); transpose(y)]
What a hazzle...
I'm trying to create a vector of integers and missing values. This works fine:
b = [4, missing, missing, 3]
But I would actually like the vector to be longer with more missing values and therefore use repeat(), but this doesn't work
append!([1,2,3], repeat([missing], 1000))
and this also doesn't work
[1,2,3, repeat([missing], 1000)]
Please, help me out, here.
It is also worth to note that if you do not need to do an in-place operation with append! actually in such cases it is much easier to do vertical concatenation:
julia> [[1, 2, 3]; repeat([missing], 2); 4; 5] # note ; that denotes vcat
7-element Array{Union{Missing, Int64},1}:
1
2
3
missing
missing
4
5
julia> vcat([1,2,3], repeat([missing], 2), 4, 5) # this is the same but using a different syntax
7-element Array{Union{Missing, Int64},1}:
1
2
3
missing
missing
4
5
The benefit of vcat is that it automatically does the type promotion (as opposed to append! in which case you have to correctly specify the eltype of the target container before the operation).
Note that because vcat does automatic type promotion in corner cases you might get a different eltype of the result of the operation:
julia> x = [1, 2, 3]
3-element Array{Int64,1}:
1
2
3
julia> append!(x, [1.0, 2.0]) # conversion from Float64 to Int happens here
5-element Array{Int64,1}:
1
2
3
1
2
julia> [[1, 2, 3]; [1.0, 2.0]] # promotion of Int to Float64 happens in this case
5-element Array{Float64,1}:
1.0
2.0
3.0
1.0
2.0
See also https://docs.julialang.org/en/v1/manual/arrays/#man-array-literals.
This will work:
append!(Union{Int,Missing}[1,2,3], repeat([missing], 1000))
[1,2,3] creates just a Vector{Int} and since Julia is strongly typed the Vector{Int} cannot accept values of non-Int type. Hence, when defining a structure, that you plan to hold more data types within, you need to explicitly state it - here we have defined Vector{Union{Int,Missing}}.
I would like to broadcast to subarrays (i.e. broadcast to slices of array). This is useful in GPU programming for example I'd like to have:
X,Y,Z = (rand(3,3,3) for _=1:3)
#.[1,2] X = f(2X^2 + 6X^3 - sqrt(X)) + Y*Z
where #.[1,2] means broadcasting along dim 3, i.e. apply colons to dim 1 and 2 in the expression.
Is there a way to support this "sub-broadcast"?
Edit: add an example
julia> a = reshape(1:8, (2,2,2))
2×2×2 Base.ReshapedArray{Int64,3,UnitRange{Int64},Tuple{}}:
[:, :, 1] =
1 3
2 4
[:, :, 2] =
5 7
6 8
julia> broadcast(*, a, a)
2×2×2 Array{Int64,3}:
[:, :, 1] =
1 9
4 16
[:, :, 2] =
25 49
36 64
julia> broadcast(*, a, a, dim=3) # I would like to broadcast the matrix multiplication (batch mode) instead of elementwise multiplication.
2×2×2 Array{Int64,3}:
[:, :, 1] =
7 15
10 22
[:, :, 2] =
67 91
78 106
Edit 2: I am trying different vectorization methods here https://arrayfire.com/introduction-to-vectorization/ via the ArrayFire.jl package (a wrapper of arrayfire), including vectorization, parallel for-loops, batching, and advanced vectorizations. arrayfire has the gfor (http://arrayfire.org/docs/page_gfor.htm) method to run parallel computations on slices of matrices, and is implemented via broadcast in ArrayFire.jl. Currently, julia's broadcast acts element-wise. I just wonder if it can act "slice-wise" then it can do pure julia 3D and 4D support for Linear Algebra functions (https://github.com/arrayfire/arrayfire/issues/483).
Of course normal nested for loops will get the job done. I am just exited about the broadcast . syntax, and wonder if it can be extend.
I think you're looking for mapslices.
mapslices(x->x*x, a, (1,2))
2×2×2 Array{Int64,3}:
[:, :, 1] =
7 15
10 22
[:, :, 2] =
67 91
78 106
mapslices(f, A, dims)
Transform the given dimensions of array A using function f. f is
called on each slice of A of the form A[...,:,...,:,...]. dims is an
integer vector specifying where the colons go in this expression.
The results are concatenated along the remaining dimensions. For
example, if dims is [1,2] and A is 4-dimensional, f is called on
A[:,:,i,j] for all i and j.
Use setdiff if you want to specify which dimension to concatenate along instead of on which to apply the function.
(If you need a multi-argument version check out this gist https://gist.github.com/alexmorley/e585df0d8d857d7c9e4a5af75df43d00)
Suppose we have an array defined like this:
a=[1 2; 3 4; 5 5; 7 9; 1 2];
In Matlab, we could find the maximum values by writing:
[x y] = max(a)
x =
7 9
In Julia, we could use:
a=[1 2; 3 4; 5 5; 7 9; 1 2]
findmax(a,1)
returning:
([7 9],
[4 9])
However, I am interested not only in finding [7 9] for the two columns, but also their relative position within each column, like [4, 4]. Of course, I can write a bit more of coding lines, but can I do it directly with findmax?
The second matrix returned by findmax is the linear index of the locations of the maxima over the entire array. You want the position within each column; to get that, you can convert the linear indices into subscripts with ind2sub. Then the first element of the subscript tuple is your row index.
julia> vals, inds = findmax(a, 1)
(
[7 9],
[4 9])
julia> map(x->ind2sub(a, x), inds)
1×2 Array{Tuple{Int64,Int64},2}:
(4,1) (4,2)
julia> map(x->ind2sub(a, x)[1], inds)
1×2 Array{Int64,2}:
4 4
This is mentioned in the comments but I figured I'd do a response that's easy to see. I have version 1.0.3, so I don't know what's the earliest version that allows this. But now you can just do
julia> findmax(a) #Returns 2D index of overall maximum value
(9, CartesianIndex(4, 2))
julia> findmax(a[:,1]) #Returns 1D index of max value in column 1
(7, 4)
julia> findmax(a[:,2]) #Returns 1D index of max value in column 2
(9, 4)
Hope this makes things easier.
I've adopted the following function:
indmaxC(x) = cat(1, [indmax(x[:,c]) for c in 1:size(x,2)]...)
The Good: it's convenient and small
The Bad: it's only valid for 2-D arrays
A safer version would be:
function indmaxC(x::AbstractArray)
assert(ndims(x)==2)
cat(1, [indmax(x[:,c]) for c in 1:size(x,2)]...)
end