MLUtilsSome general utility functions. Many of them were part of Flux.jl
"""
unsqueeze(x; dims)
Return `x` reshaped into an array one dimensionality higher than `x`,
where `dims` indicates in which dimension `x` is extended.
`dims` can be an integer between 1 and `ndims(x)+1`.
See also [`flatten`](@ref), [`stack`](@ref).
# Examples
```jldoctest
julia> unsqueeze([1 2; 3 4], dims=2)
2×1×2 Array{Int64, 3}:
[:, :, 1] =
1
3
[:, :, 2] =
2
4
julia> xs = [[1, 2], [3, 4], [5, 6]]
3-element Vector{Vector{Int64}}:
[1, 2]
[3, 4]
[5, 6]
julia> unsqueeze(xs, dims=1)
1×3 Matrix{Vector{Int64}}:
[1, 2] [3, 4] [5, 6]
```
"""
function
unsqueeze
(
x
::
AbstractArray
{
T
,
N
}
;
dims
::
Int
)
where
{
T
,
N
}
@
assert
1
<=
dims
<=
N
+
1
sz
=
ntuple
(
i
->
i
<
dims
?
size
(
x
,
i
)
:
i
==
dims
?
1
:
size
(
x
,
i
-
1
)
,
N
+
1
)
return
reshape
(
x
,
sz
)
end
"""
unsqueeze(; dims)
Returns a function which, acting on an array, inserts a dimension of size 1 at `dims`.
# Examples
```jldoctest
julia> rand(21, 22, 23) |> unsqueeze(dims=2) |> size
(21, 1, 22, 23)
```
"""
unsqueeze
(
;
dims
::
Int
)
=
Base
.
Fix2
(
_unsqueeze
,
dims
)
_unsqueeze
(
x
,
dims
)
=
unsqueeze
(
x
;
dims
)
Base
.
show_function
(
io
::
IO
,
u
::
Base
.
Fix2
{
typeof
(
_unsqueeze
)
}
,
::
Bool
)
=
print
(
io
,
"
unsqueeze(dims=
"
,
u
.
x
,
"
)
"
)
"""
unstack(xs; dims)
Unroll the given `xs` into an array of arrays along the given dimension `dims`.
See also [`stack`](@ref), [`unbatch`](@ref),
and [`chunk`](@ref).
# Examples
```jldoctest
julia> unstack([1 3 5 7; 2 4 6 8], dims=2)
4-element Vector{Vector{Int64}}:
[1, 2]
[3, 4]
[5, 6]
[7, 8]
```
"""
unstack
(
xs
;
dims
)
=
_unstack
(
dims
,
xs
)
dims2unstack
(
::
Val
{
dims
}
)
where
dims
=
dims2unstack
(
dims
)
dims2unstack
(
dims
::
Integer
)
=
dims
@
inline
_unstack
(
dims
,
xs
)
=
[
copy
(
selectdim
(
xs
,
dims2unstack
(
dims
)
,
i
)
)
for
i
in
axes
(
xs
,
dims2unstack
(
dims
)
)
]
"""
chunk(x, n; [dims])
chunk(x; [size, dims])
Split `x` into `n` parts or alternatively, if `size` is an integer, into equal chunks of size `size`.
The parts contain the same number of elements except possibly for the last one that can be smaller.
In case `size` is a collection of integers instead, the elements of `x` are split into chunks of
the given sizes.
If `x` is an array, `dims` can be used to specify along which dimension to
split (defaults to the last dimension).
# Examples
```jldoctest
julia> chunk(1:10, 3)
3-element Vector{UnitRange{Int64}}:
1:4
5:8
9:10
julia> chunk(1:10; size = 2)
5-element Vector{UnitRange{Int64}}:
1:2
3:4
5:6
7:8
9:10
julia> x = reshape(collect(1:20), (5, 4))
5×4 Matrix{Int64}:
1 6 11 16
2 7 12 17
3 8 13 18
4 9 14 19
5 10 15 20
julia> xs = chunk(x, 2, dims=1)
2-element Vector{SubArray{Int64, 2, Matrix{Int64}, Tuple{UnitRange{Int64}, Base.Slice{Base.OneTo{Int64}}}, false}}:
[1 6 11 16; 2 7 12 17; 3 8 13 18]
[4 9 14 19; 5 10 15 20]
julia> xs[1]
3×4 view(::Matrix{Int64}, 1:3, :) with eltype Int64:
1 6 11 16
2 7 12 17
3 8 13 18
julia> xes = chunk(x; size = 2, dims = 2)
2-element Vector{SubArray{Int64, 2, Matrix{Int64}, Tuple{Base.Slice{Base.OneTo{Int64}}, UnitRange{Int64}}, true}}:
[1 6; 2 7; … ; 4 9; 5 10]
[11 16; 12 17; … ; 14 19; 15 20]
julia> xes[2]
5×2 view(::Matrix{Int64}, :, 3:4) with eltype Int64:
11 16
12 17
13 18
14 19
15 20
julia> chunk(1:6; size = [2, 4])
2-element Vector{UnitRange{Int64}}:
1:2
3:6
```
"""
chunk
(
x
;
size
::
Int
)
=
collect
(
Iterators
.
partition
(
x
,
size
)
)
chunk
(
x
,
n
::
Int
)
=
chunk
(
x
;
size
=
cld
(
length
(
x
)
,
n
)
)
chunk
(
x
::
AbstractArray
,
n
::
Int
;
dims
::
Int
=
ndims
(
x
)
)
=
chunk
(
x
;
size
=
cld
(
size
(
x
,
dims
)
,
n
)
,
dims
)
function
chunk
(
x
::
AbstractArray
;
size
,
dims
::
Int
=
ndims
(
x
)
)
idxs
=
_partition_idxs
(
x
,
size
,
dims
)
return
[
_selectdim
(
x
,
dims
,
i
)
for
i
in
idxs
]
end
"""
chunk(x, partition_idxs; [npartitions, dims])
Partition the array `x` along the dimension `dims` according to the indexes
in `partition_idxs`.
`partition_idxs` must be sorted and contain only positive integers
between 1 and the number of partitions.
If the number of partition `npartitions` is not provided,
it is inferred from `partition_idxs`.
If `dims` is not provided, it defaults to the last dimension.
See also [`unbatch`](@ref).
# Examples
```jldoctest
julia> x = reshape([1:10;], 2, 5)
2×5 Matrix{Int64}:
1 3 5 7 9
2 4 6 8 10
julia> chunk(x, [1, 2, 2, 3, 3])
3-element Vector{SubArray{Int64, 2, Matrix{Int64}, Tuple{Base.Slice{Base.OneTo{Int64}}, UnitRange{Int64}}, true}}:
[1; 2;;]
[3 5; 4 6]
[7 9; 8 10]
```
"""
function
chunk
(
x
::
AbstractArray
{
T
,
N
}
,
partition_idxs
::
AbstractVector
;
npartitions
=
nothing
,
dims
=
ndims
(
x
)
)
where
{
T
,
N
}
@
assert
issorted
(
partition_idxs
)
"
partition_idxs must be sorted
"
m
=
npartitions
===
nothing
?
maximum
(
partition_idxs
)
:
npartitions
degrees
=
NNlib
.
scatter
(
+
,
ones_like
(
partition_idxs
)
,
partition_idxs
,
dstsize
=
(
m
,
)
)
return
chunk
(
x
;
size
=
degrees
,
dims
)
endwork around https://github.com/JuliaML/MLUtils.jl/issues/103
_selectdim
(
x
::
AbstractArray
,
dims
::
Int
,
i
)
=
selectdim
(
x
,
dims
,
i
)
_selectdim
(
x
::
AbstractArray
,
dims
::
Int
,
i
::
UnitRange
)
=
_selectdim
(
x
,
Val
(
dims
)
,
i
)
function
_selectdim
(
x
::
AbstractArray
{
T
,
N
}
,
::
Val
{
dims
}
,
i
::
UnitRange
)
where
{
T
,
N
,
dims
}
return
view
(
x
,
ntuple
(
_
->
Colon
(
)
,
dims
-
1
)
...
,
i
,
ntuple
(
_
->
Colon
(
)
,
N
-
dims
)
...
)
end
function
rrule
(
::
typeof
(
chunk
)
,
x
::
AbstractArray
;
size
,
dims
::
Int
=
ndims
(
x
)
)
# This is the implementation of chunk
idxs
=
_partition_idxs
(
x
,
size
,
dims
)
y
=
[
_selectdim
(
x
,
dims
,
i
)
for
i
in
idxs
]
valdims
=
Val
(
dims
)
# TODO avoid capturing x in the pullback
chunk_pullback
(
dy
)
=
(
NoTangent
(
)
,
∇chunk
(
unthunk
(
dy
)
,
x
,
idxs
,
valdims
)
)
return
y
,
chunk_pullback
end
_partition_idxs
(
x
,
size
::
Int
,
dims
::
Int
)
=
Iterators
.
partition
(
axes
(
x
,
dims
)
,
size
)
_partition_idxs
(
x
,
size
,
dims
::
Int
)
=
_partition_idxs
(
x
,
collect
(
size
)
,
dims
)
function
_partition_idxs
(
x
,
size
::
AbstractVector
{
<:
Integer
}
,
dims
::
Int
)
n
=
length
(
axes
(
x
,
dims
)
)
cumsz
=
cumsum
(
size
)
if
cumsz
[
end
]
!=
n
throw
(
ArgumentError
(
"
The sum of the sizes must be equal to
$
n
, the length of the dimension.
"
)
)
end
return
[
(
i
==
1
?
1
:
cumsz
[
i
-
1
]
+
1
)
:
cumsz
[
i
]
for
i
=
1
:
length
(
cumsz
)
]
end
@
non_differentiable
_partition_idxs
(
::
Any
...
)Similar to ∇eachslice https://github.com/JuliaDiff/ChainRules.jl/blob/8108a77a96af5d4b0c460aac393e44f8943f3c5e/src/rulesets/Base/indexing.jl#L77
function
∇chunk
(
dys
,
x
,
idxs
,
vd
::
Val
{
dim
}
)
where
{
dim
}
i1
=
findfirst
(
dy
->
!
(
dy
isa
AbstractZero
)
,
dys
)
if
i1
===
nothing
# all slices are Zero!
return
_zero_fill!
(
similar
(
x
,
float
(
eltype
(
x
)
)
)
)
end
T
=
promote_type
(
eltype
(
dys
[
i1
]
)
,
eltype
(
x
)
)
# The whole point of this gradient is that we can allocate one `dx` array:
dx
=
similar
(
x
,
T
)
for
(
k
,
i
)
in
enumerate
(
idxs
)
slice
=
_selectdim
(
dx
,
dim
,
i
)
if
dys
[
k
]
isa
AbstractZero
_zero_fill!
(
slice
)
# Avoids this: copyto!([1,2,3], ZeroTangent()) == [0,2,3]
else
copyto!
(
slice
,
dys
[
k
]
)
end
end
return
ProjectTo
(
x
)
(
dx
)
end
_zero_fill!
(
dx
::
AbstractArray
{
<:
Number
}
)
=
fill!
(
dx
,
zero
(
eltype
(
dx
)
)
)
_zero_fill!
(
dx
::
AbstractArray
)
=
map!
(
zero
,
dx
,
dx
)
function
rrule
(
::
typeof
(
∇chunk
)
,
dys
,
x
,
idxs
,
vd
::
Val
{
dim
}
)
where
dim
n
=
length
(
dys
)
function
∇∇chunk
(
dz_raw
)
dz
=
chunk
(
unthunk
(
dz_raw
)
,
n
;
dims
=
dim
)
return
(
NoTangent
(
)
,
dz
,
NoTangent
(
)
,
NoTangent
(
)
,
NoTangent
(
)
)
end
return
∇chunk
(
dys
,
x
,
idxs
,
vd
)
,
∇∇chunk
end
"""
group_counts(x)
Count the number of times that each element of `x` appears.
See also [`group_indices`](@ref)
# Examples
```jldoctest
julia> group_counts(['a', 'b', 'b'])
Dict{Char, Int64} with 2 entries:
'a' => 1
'b' => 2
```
"""
function
group_counts
(
x
)
fs
=
Dict
{
eltype
(
x
)
,
Int
}
(
)
for
a
in
x
fs
[
a
]
=
get
(
fs
,
a
,
0
)
+
1
end
return
fs
end
"""
group_indices(x) -> Dict
Computes the indices of elements in the vector `x` for each distinct value contained.
This information is useful for resampling strategies, such as stratified sampling.
See also [`group_counts`](@ref).
# Examples
```jldoctest
julia> x = [:yes, :no, :maybe, :yes];
julia> group_indices(x)
Dict{Symbol, Vector{Int64}} with 3 entries:
:yes => [1, 4]
:maybe => [3]
:no => [2]
```
"""
function
group_indices
(
classes
::
T
)
where
T
<:
AbstractVector
dict
=
Dict
{
eltype
(
T
)
,
Vector
{
Int
}
}
(
)
for
(
idx
,
elem
)
in
enumerate
(
classes
)
if
!
haskey
(
dict
,
elem
)
push!
(
dict
,
elem
=>
[
idx
]
)
else
push!
(
dict
[
elem
]
,
idx
)
end
end
return
dict
end
"""
batch(xs)
Batch the arrays in `xs` into a single array with
an extra dimension.
If the elements of `xs` are tuples, named tuples, or dicts,
the output will be of the same type.
See also [`unbatch`](@ref).
# Examples
```jldoctest
julia> batch([[1,2,3],
[4,5,6]])
3×2 Matrix{Int64}:
1 4
2 5
3 6
julia> batch([(a=[1,2], b=[3,4])
(a=[5,6], b=[7,8])])
(a = [1 5; 2 6], b = [3 7; 4 8])
```
"""
function
batch
(
xs
)
# Fallback for generric iterables
@
assert
length
(
xs
)
>
0
"
Input should be non-empty
"
data
=
first
(
xs
)
isa
AbstractArray
?
similar
(
first
(
xs
)
,
size
(
first
(
xs
)
)
...
,
length
(
xs
)
)
:
Vector
{
eltype
(
xs
)
}
(
undef
,
length
(
xs
)
)
for
(
i
,
x
)
in
enumerate
(
xs
)
data
[
batchindex
(
data
,
i
)
...
]
=
x
end
return
data
end
batchindex
(
xs
,
i
)
=
(
reverse
(
Base
.
tail
(
reverse
(
axes
(
xs
)
)
)
)
...
,
i
)
batch
(
xs
::
AbstractArray
{
<:
AbstractArray
}
)
=
stack
(
xs
)
function
batch
(
xs
::
Vector
{
<:
Tuple
}
)
@
assert
length
(
xs
)
>
0
"
Input should be non-empty
"
n
=
length
(
first
(
xs
)
)
@
assert
all
(
length
.
(
xs
)
.==
n
)
"
Cannot batch tuples with different lengths
"
return
ntuple
(
i
->
batch
(
[
x
[
i
]
for
x
in
xs
]
)
,
n
)
end
function
batch
(
xs
::
Vector
{
<:
NamedTuple
}
)
@
assert
length
(
xs
)
>
0
"
Input should be non-empty
"
all_keys
=
[
sort
(
collect
(
keys
(
x
)
)
)
for
x
in
xs
]
ks
=
all_keys
[
1
]
@
assert
all
(
==
(
ks
)
,
all_keys
)
"
Cannot batch named tuples with different keys
"
NamedTuple
(
k
=>
batch
(
[
x
[
k
]
for
x
in
xs
]
)
for
k
in
ks
)
end
function
batch
(
xs
::
Vector
{
<:
Dict
}
)
@
assert
length
(
xs
)
>
0
"
Input should be non-empty
"
all_keys
=
[
sort
(
collect
(
keys
(
x
)
)
)
for
x
in
xs
]
ks
=
all_keys
[
1
]
@
assert
all
(
==
(
ks
)
,
all_keys
)
"
cannot batch dicts with different keys
"
Dict
(
k
=>
batch
(
[
x
[
k
]
for
x
in
xs
]
)
for
k
in
ks
)
end
"""
unbatch(x)
Reverse of the [`batch`](@ref) operation,
unstacking the last dimension of the array `x`.
See also [`unstack`](@ref) and [`chunk`](@ref).
# Examples
```jldoctest
julia> unbatch([1 3 5 7;
2 4 6 8])
4-element Vector{Vector{Int64}}:
[1, 2]
[3, 4]
[5, 6]
[7, 8]
```
"""
unbatch
(
x
::
AbstractArray
)
=
[
getobs
(
x
,
i
)
for
i
in
1
:
numobs
(
x
)
]
unbatch
(
x
::
AbstractVector
)
=
x
"""
batchseq(seqs, val = 0)
Take a list of `N` sequences, and turn them into a single sequence where each
item is a batch of `N`. Short sequences will be padded by `val`.
# Examples
```jldoctest
julia> batchseq([[1, 2, 3], [4, 5]], 0)
3-element Vector{Vector{Int64}}:
[1, 4]
[2, 5]
[3, 0]
```
"""
function
batchseq
(
xs
,
val
=
0
,
n
=
nothing
)
n
=
n
===
nothing
?
maximum
(
x
->
size
(
x
,
ndims
(
x
)
)
,
xs
)
:
n
xs_
=
[
rpad_constant
(
x
,
n
,
val
;
dims
=
ndims
(
x
)
)
for
x
in
xs
]
[
batch
(
[
obsview
(
xs_
[
j
]
,
i
)
for
j
=
1
:
length
(
xs_
)
]
)
for
i
=
1
:
n
]
end
"""
rpad_constant(v::AbstractArray, n::Union{Integer, Tuple}, val = 0; dims=:)
Return the given sequence padded with `val` along the dimensions `dims`
up to a maximum length in each direction specified by `n`.
# Examples
```jldoctest
julia> rpad_constant([1, 2], 4, -1) # passing with -1 up to size 4
4-element Vector{Int64}:
1
2
-1
-1
julia> rpad_constant([1, 2, 3], 2) # no padding if length is already greater than n
3-element Vector{Int64}:
1
2
3
julia> rpad_constant([1 2; 3 4], 4; dims=1) # padding along the first dimension
4×2 Matrix{Int64}:
1 2
3 4
0 0
0 0
julia> rpad_constant([1 2; 3 4], 4) # padding along all dimensions by default
4×2 Matrix{Int64}:
1 2
3 4
0 0
0 0
```
"""
function
rpad_constant
(
x
::
AbstractArray
,
n
::
Union
{
Integer
,
Tuple
}
,
val
=
0
;
dims
=
:
)
ns
=
_rpad_pads
(
x
,
n
,
dims
)
return
NNlib
.
pad_constant
(
x
,
ns
,
val
;
dims
)
end
function
_rpad_pads
(
x
,
n
,
dims
)
_dims
=
dims
===
Colon
(
)
?
(
1
:
ndims
(
x
)
)
:
dims
_n
=
n
isa
Integer
?
ntuple
(
i
->
n
,
length
(
_dims
)
)
:
n
@
assert
length
(
_dims
)
==
length
(
_n
)
ns
=
ntuple
(
i
->
isodd
(
i
)
?
0
:
max
(
_n
[
i
÷
2
]
-
size
(
x
,
_dims
[
i
÷
2
]
)
,
0
)
,
2
*
length
(
_n
)
)
return
ns
end
@
non_differentiable
_rpad_pads
(
::
Any
...
)
"""
flatten(x::AbstractArray)
Reshape arbitrarly-shaped input into a matrix-shaped output,
preserving the size of the last dimension.
See also [`unsqueeze`](@ref).
# Examples
```jldoctest
julia> rand(3,4,5) |> flatten |> size
(12, 5)
```
"""
function
flatten
(
x
::
AbstractArray
)
return
reshape
(
x
,
:
,
size
(
x
)
[
end
]
)
end
"""
normalise(x; dims=ndims(x), ϵ=1e-5)
Normalise the array `x` to mean 0 and standard deviation 1 across the dimension(s) given by `dims`.
Per default, `dims` is the last dimension.
`ϵ` is a small additive factor added to the denominator for numerical stability.
"""
function
normalise
(
x
::
AbstractArray
;
dims
=
ndims
(
x
)
,
ϵ
=
ofeltype
(
x
,
1e-5
)
)
μ
=
mean
(
x
,
dims
=
dims
)
# σ = std(x, dims=dims, mean=μ, corrected=false) # use this when Zygote#478 gets merged
σ
=
std
(
x
,
dims
=
dims
,
corrected
=
false
)
return
(
x
.-
μ
)
./
(
σ
.+
ϵ
)
end
ofeltype
(
x
,
y
)
=
convert
(
float
(
eltype
(
x
)
)
,
y
)
epseltype
(
x
)
=
eps
(
float
(
eltype
(
x
)
)
)
"""
ones_like(x, [element_type=eltype(x)], [dims=size(x)]))
Create an array with the given element type and size, based upon the given source array `x`.
All element of the new array will be set to 1.
The second and third arguments are both optional, defaulting to the given array's eltype and
size. The dimensions may be specified as an integer or as a tuple argument.
See also [`zeros_like`](@ref) and [`fill_like`](@ref).
# Examples
```julia-repl
julia> x = rand(Float32, 2)
2-element Vector{Float32}:
0.8621633
0.5158395
julia> ones_like(x, (3, 3))
3×3 Matrix{Float32}:
1.0 1.0 1.0
1.0 1.0 1.0
1.0 1.0 1.0
julia> using CUDA
julia> x = CUDA.rand(2, 2)
2×2 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
0.82297 0.656143
0.701828 0.391335
julia> ones_like(x, Float64)
2×2 CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}:
1.0 1.0
1.0 1.0
```
"""
ones_like
(
x
::
AbstractArray
,
T
::
Type
,
sz
=
size
(
x
)
)
=
fill!
(
similar
(
x
,
T
,
sz
)
,
1
)
ones_like
(
x
::
AbstractArray
,
sz
=
size
(
x
)
)
=
ones_like
(
x
,
eltype
(
x
)
,
sz
)
"""
zeros_like(x, [element_type=eltype(x)], [dims=size(x)]))
Create an array with the given element type and size, based upon the given source array `x`.
All element of the new array will be set to 0.
The second and third arguments are both optional, defaulting to the given array's eltype and
size. The dimensions may be specified as an integer or as a tuple argument.
See also [`ones_like`](@ref) and [`fill_like`](@ref).
# Examples
```julia-repl
julia> x = rand(Float32, 2)
2-element Vector{Float32}:
0.4005432
0.36934233
julia> zeros_like(x, (3, 3))
3×3 Matrix{Float32}:
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
julia> using CUDA
julia> x = CUDA.rand(2, 2)
2×2 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
0.0695155 0.667979
0.558468 0.59903
julia> zeros_like(x, Float64)
2×2 CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}:
0.0 0.0
0.0 0.0
```
"""
zeros_like
(
x
::
AbstractArray
,
T
::
Type
,
sz
=
size
(
x
)
)
=
fill!
(
similar
(
x
,
T
,
sz
)
,
0
)
zeros_like
(
x
::
AbstractArray
,
sz
=
size
(
x
)
)
=
zeros_like
(
x
,
eltype
(
x
)
,
sz
)
"""
rand_like([rng=default_rng()], x, [element_type=eltype(x)], [dims=size(x)])
Create an array with the given element type and size, based upon the given source array `x`.
All element of the new array will be set to a random value.
The last two arguments are both optional, defaulting to the given array's eltype and
size. The dimensions may be specified as an integer or as a tuple argument.
The default random number generator is used, unless a custom one is passed in explicitly
as the first argument.
See also `Base.rand` and [`randn_like`](@ref).
# Examples
```julia-repl
julia> x = ones(Float32, 2)
2-element Vector{Float32}:
1.0
1.0
julia> rand_like(x, (3, 3))
3×3 Matrix{Float32}:
0.780032 0.920552 0.53689
0.121451 0.741334 0.5449
0.55348 0.138136 0.556404
julia> using CUDA
julia> CUDA.ones(2, 2)
2×2 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
1.0 1.0
1.0 1.0
julia> rand_like(x, Float64)
2×2 CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}:
0.429274 0.135379
0.718895 0.0098756
```
"""
rand_like
(
x
::
AbstractArray
,
T
::
Type
,
sz
=
size
(
x
)
)
=
rand!
(
similar
(
x
,
T
,
sz
)
)
rand_like
(
x
::
AbstractArray
,
sz
=
size
(
x
)
)
=
rand_like
(
x
,
eltype
(
x
)
,
sz
)
rand_like
(
rng
::
AbstractRNG
,
x
::
AbstractArray
,
T
::
Type
,
sz
=
size
(
x
)
)
=
rand!
(
rng
,
similar
(
x
,
T
,
sz
)
)
rand_like
(
rng
::
AbstractRNG
,
x
::
AbstractArray
,
sz
=
size
(
x
)
)
=
rand_like
(
rng
,
x
,
eltype
(
x
)
,
sz
)
"""
randn_like([rng=default_rng()], x, [element_type=eltype(x)], [dims=size(x)])
Create an array with the given element type and size, based upon the given source array `x`.
All element of the new array will be set to a random value drawn from a normal distribution.
The last two arguments are both optional, defaulting to the given array's eltype and
size. The dimensions may be specified as an integer or as a tuple argument.
The default random number generator is used, unless a custom one is passed in explicitly
as the first argument.
See also `Base.randn` and [`rand_like`](@ref).
# Examples
```julia-repl
julia> x = ones(Float32, 2)
2-element Vector{Float32}:
1.0
1.0
julia> randn_like(x, (3, 3))
3×3 Matrix{Float32}:
-0.385331 0.956231 0.0745102
1.43756 -0.967328 2.06311
0.0482372 1.78728 -0.902547
julia> using CUDA
julia> CUDA.ones(2, 2)
2×2 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
1.0 1.0
1.0 1.0
julia> randn_like(x, Float64)
2×2 CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}:
-0.578527 0.823445
-1.01338 -0.612053
```
"""
randn_like
(
x
::
AbstractArray
,
T
::
Type
,
sz
=
size
(
x
)
)
=
randn!
(
similar
(
x
,
T
,
sz
)
)
randn_like
(
x
::
AbstractArray
,
sz
=
size
(
x
)
)
=
randn_like
(
x
,
eltype
(
x
)
,
sz
)
randn_like
(
rng
::
AbstractRNG
,
x
::
AbstractArray
,
T
::
Type
,
sz
=
size
(
x
)
)
=
randn!
(
rng
,
similar
(
x
,
T
,
sz
)
)
randn_like
(
rng
::
AbstractRNG
,
x
::
AbstractArray
,
sz
=
size
(
x
)
)
=
randn_like
(
rng
,
x
,
eltype
(
x
)
,
sz
)
"""
fill_like(x, val, [element_type=eltype(x)], [dims=size(x)]))
Create an array with the given element type and size, based upon the given source array `x`.
All element of the new array will be set to `val`.
The third and fourth arguments are both optional, defaulting to the given array's eltype and
size. The dimensions may be specified as an integer or as a tuple argument.
See also [`zeros_like`](@ref) and [`ones_like`](@ref).
# Examples
```julia-repl
julia> x = rand(Float32, 2)
2-element Vector{Float32}:
0.16087806
0.89916044
julia> fill_like(x, 1.7, (3, 3))
3×3 Matrix{Float32}:
1.7 1.7 1.7
1.7 1.7 1.7
1.7 1.7 1.7
julia> using CUDA
julia> x = CUDA.rand(2, 2)
2×2 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
0.803167 0.476101
0.303041 0.317581
julia> fill_like(x, 1.7, Float64)
2×2 CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}:
1.7 1.7
1.7 1.7
```
"""
fill_like
(
x
::
AbstractArray
,
val
,
T
::
Type
,
sz
=
size
(
x
)
)
=
fill!
(
similar
(
x
,
T
,
sz
)
,
val
)
fill_like
(
x
::
AbstractArray
,
val
,
sz
=
size
(
x
)
)
=
fill_like
(
x
,
val
,
eltype
(
x
)
,
sz
)
@
non_differentiable
zeros_like
(
::
Any
...
)
@
non_differentiable
ones_like
(
::
Any
...
)
@
non_differentiable
rand_like
(
::
Any
...
)
@
non_differentiable
randn_like
(
::
Any
...
)
function
rrule
(
::
typeof
(
fill_like
)
,
x
::
AbstractArray
,
val
,
T
::
Type
,
sz
)
function
fill_like_pullback
(
Δ
)
return
(
NoTangent
(
)
,
ZeroTangent
(
)
,
sum
(
Δ
)
,
NoTangent
(
)
,
NoTangent
(
)
)
end
return
fill_like
(
x
,
val
,
T
,
sz
)
,
fill_like_pullback
end