8000 GitHub - FluxML/OneHotArrays.jl: Memory efficient one-hot array encodings
[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to content

FluxML/OneHotArrays.jl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

83 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

OneHotArrays.jl

Documentation Tests

This package provides memory efficient one-hot array encodings. It was originally part of Flux.jl.

julia> using OneHotArrays

julia> m = onehotbatch([10, 20, 30, 10, 10], 10:10:40)
4×5 OneHotMatrix(::Vector{UInt32}) with eltype Bool:
 1      1  1
   1      
     1    
         

julia> dump(m)
OneHotMatrix{UInt32, Vector{UInt32}}
  indices: Array{UInt32}((5,)) UInt32[0x00000001, 0x00000002, 0x00000003, 0x00000001, 0x00000001]
  nlabels: Int64 4

julia> @which rand(100, 4) * m
*(A::AbstractMatrix, B::Union{OneHotArray{var"#s29", 1, var"N+1", I}, Base.ReshapedArray{Bool, var"N+1", <:OneHotArray{var"#s29", <:Any, <:Any, I}}} where {var"#s29", var"N+1", I})
     @ OneHotArrays ~/.julia/packages/OneHotArrays/DqU9m/src/linalg.jl:7

About

Memory efficient one-hot array encodings

Resources

License

Code of conduct

Stars

Watchers

Forks

Sponsor this project

 

Packages

No packages published

Contributors 11

Languages

0