using OneHotArrays.jl

Author: Saransh-cpp

docs/Project.toml

@@ -4,6 +4,7 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
+OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 
 [compat]

docs/make.jl

@@ -1,10 +1,10 @@
-using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers
+using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays
 
 
 DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive = true)
 
 makedocs(
-    modules = [Flux, NNlib, Functors, MLUtils, BSON, Optimisers],
+    modules = [Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays],
     doctest = false,
     sitename = "Flux",
     strict = [:cross_references],

docs/src/data/onehot.md

@@ -1,9 +1,9 @@
-# One-Hot Encoding
+# One-Hot Encoding with OneHotArrays.jl
 
-It's common to encode categorical variables (like `true`, `false` or `cat`, `dog`) in "one-of-k" or ["one-hot"](https://en.wikipedia.org/wiki/One-hot) form. Flux provides the `onehot` function to make this easy.
+It's common to encode categorical variables (like `true`, `false` or `cat`, `dog`) in "one-of-k" or ["one-hot"](https://en.wikipedia.org/wiki/One-hot) form. [OneHotArrays.jl](https://github.com/FluxML/OneHotArrays.jl) provides the `onehot` function to make this easy.
 
 ```jldoctest onehot
-julia> using Flux: onehot, onecold
+julia> using OneHotArrays
 
 julia> onehot(:b, [:a, :b, :c])
 3-element OneHotVector(::UInt32) with eltype Bool:
@@ -34,7 +34,7 @@ julia> onecold([0.3, 0.2, 0.5], [:a, :b, :c])
 For multiple samples at once, `onehotbatch` creates a batch (matrix) of one-hot vectors, and `onecold` treats matrices as batches.
 
 ```jldoctest onehot
-julia> using Flux: onehotbatch
+julia> using OneHotArrays
 
 julia> onehotbatch([:b, :a, :b], [:a, :b, :c])
 3×3 OneHotMatrix(::Vector{UInt32}) with eltype Bool:
@@ -52,7 +52,9 @@ julia> onecold(ans, [:a, :b, :c])
 Note that these operations returned `OneHotVector` and `OneHotMatrix` rather than `Array`s. `OneHotVector`s behave like normal vectors but avoid any unnecessary cost compared to using an integer index directly. For example, multiplying a matrix with a one-hot vector simply slices out the relevant row of the matrix under the hood.
 
 ```@docs
-Flux.onehot
-Flux.onecold
-Flux.onehotbatch
+OneHotArrays.onehot
+OneHotArrays.onecold
+OneHotArrays.onehotbatch
+OneHotArrays.OneHotVector
+OneHotArrays.OneHotMatrix
 ```

src/layers/basic.jl

@@ -651,7 +651,7 @@ for a vocabulary of size `in`.
 
 This layer is often used to store word embeddings and retrieve them using indices. 
 The input to the layer can be either a vector of indexes
-or the corresponding [`onehot encoding`](@ref Flux.onehotbatch). 
+or the corresponding [`onehot encoding`](@ref OneHotArrays.onehotbatch). 
 
 # Examples
 ```jldoctest