45 lines
1.3 KiB
Julia
45 lines
1.3 KiB
Julia
struct OneHotVector <: AbstractVector{Bool}
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ix::UInt32
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of::UInt32
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end
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Base.size(xs::OneHotVector) = (Int64(xs.of),)
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Base.getindex(xs::OneHotVector, i::Integer) = i == xs.ix
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Base.:*(A::AbstractMatrix, b::OneHotVector) = A[:, b.ix]
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struct OneHotMatrix{A<:AbstractVector{OneHotVector}} <: AbstractMatrix{Bool}
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height::Int
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data::A
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end
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Base.size(xs::OneHotMatrix) = (Int64(xs.height),length(xs.data))
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Base.getindex(xs::OneHotMatrix, i::Int, j::Int) = xs.data[j][i]
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Base.:*(A::AbstractMatrix, B::OneHotMatrix) = A[:, map(x->x.ix, B.data)]
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Base.hcat(x::OneHotVector, xs::OneHotVector...) = OneHotMatrix(length(x), [x, xs...])
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batch(xs::AbstractArray{<:OneHotVector}) = OneHotMatrix(length(first(xs)), xs)
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import NNlib.adapt
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adapt(T, xs::OneHotMatrix) = OneHotMatrix(xs.height, adapt(T, xs.data))
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@require CuArrays begin
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import CuArrays: CuArray, cudaconvert
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Base.Broadcast._containertype(::Type{<:OneHotMatrix{<:CuArray}}) = CuArray
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cudaconvert(x::OneHotMatrix{<:CuArray}) = OneHotMatrix(x.height, cudaconvert(x.data))
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end
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onehot(l, labels) = OneHotVector(findfirst(labels, l), length(labels))
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onehotbatch(ls, labels) = OneHotMatrix(length(labels), [onehot(l, labels) for l in ls])
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argmax(y::AbstractVector, labels = 1:length(y)) =
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labels[findfirst(y, maximum(y))]
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argmax(y::AbstractMatrix, l...) =
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squeeze(mapslices(y -> argmax(y, l...), y, 1), 1)
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