diff --git a/dev/index.html b/dev/index.html index 4d07d40..0630504 100644 --- a/dev/index.html +++ b/dev/index.html @@ -15,4 +15,4 @@ :b julia> Flux.onecold([0.3, 0.2, 0.5], (:a, :b, :c)) -:c

In addition to functions for encoding and decoding data as one-hot, this package provides numerous "fast-paths" for linear algebraic operations with one-hot arrays. For example, multiplying by a matrix by a one-hot vector triggers an indexing operation instead of a matrix multiplication.

+:c

In addition to functions for encoding and decoding data as one-hot, this package provides numerous "fast-paths" for linear algebraic operations with one-hot arrays. For example, multiplying by a matrix by a one-hot vector triggers an indexing operation instead of a matrix multiplication.

diff --git a/dev/reference/index.html b/dev/reference/index.html index 5594089..bb0af7b 100644 --- a/dev/reference/index.html +++ b/dev/reference/index.html @@ -10,7 +10,7 @@ 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 ], 'a':'e') |> String -"abeacadabea"source
OneHotArrays.onehot — Method
onehot(x, labels, [default])

Returns a OneHotVector which is roughly a sparse representation of x .== labels.

Instead of storing say Vector{Bool}, it stores the index of the first occurrence of x in labels. If x is not found in labels, then it either returns onehot(default, labels), or gives an error if no default is given.

See also onehotbatch to apply this to many xs, and onecold to reverse either of these, as well as to generalise argmax.

Examples

julia> β = onehot(:b, (:a, :b, :c))
+"abeacadabea"
source
OneHotArrays.onehot — Method
onehot(x, labels, [default])

Returns a OneHotVector which is roughly a sparse representation of x .== labels.

Instead of storing say Vector{Bool}, it stores the index of the first occurrence of x in labels. If x is not found in labels, then it either returns onehot(default, labels), or gives an error if no default is given.

See also onehotbatch to apply this to many xs, and onecold to reverse either of these, as well as to generalise argmax.

Examples

julia> β = onehot(:b, (:a, :b, :c))
 3-element OneHotVector(::UInt32) with eltype Bool:
  ⋅
  1
@@ -23,7 +23,7 @@
 3×3 OneHotMatrix(::Vector{UInt32}) with eltype Bool:
  1  ⋅  ⋅
  ⋅  1  ⋅
- ⋅  ⋅  1
source
OneHotArrays.onehotbatch — Method
onehotbatch(xs, labels, [default])

Returns a OneHotMatrix where kth column of the matrix is onehot(xs[k], labels). This is a sparse matrix, which stores just a Vector{UInt32} containing the indices of the nonzero elements.

If one of the inputs in xs is not found in labels, that column is onehot(default, labels) if default is given, else an error.

If xs has more dimensions, N = ndims(xs) > 1, then the result is an AbstractArray{Bool, N+1} which is one-hot along the first dimension, i.e. result[:, k...] == onehot(xs[k...], labels).

Note that xs can be any iterable, such as a string. And that using a tuple for labels will often speed up construction, certainly for less than 32 classes.

Examples

julia> oh = onehotbatch("abracadabra", 'a':'e', 'e')
+ ⋅  ⋅  1
source
OneHotArrays.onehotbatch — Method
onehotbatch(xs, labels, [default])

Returns a OneHotMatrix where kth column of the matrix is onehot(xs[k], labels). This is a sparse matrix, which stores just a Vector{UInt32} containing the indices of the nonzero elements.

If one of the inputs in xs is not found in labels, that column is onehot(default, labels) if default is given, else an error.

If xs has more dimensions, N = ndims(xs) > 1, then the result is an AbstractArray{Bool, N+1} which is one-hot along the first dimension, i.e. result[:, k...] == onehot(xs[k...], labels).

Note that xs can be any iterable, such as a string. And that using a tuple for labels will often speed up construction, certainly for less than 32 classes.

Examples

julia> oh = onehotbatch("abracadabra", 'a':'e', 'e')
 5×11 OneHotMatrix(::Vector{UInt32}) with eltype Bool:
  1  ⋅  ⋅  1  ⋅  1  ⋅  1  ⋅  ⋅  1
  ⋅  1  ⋅  ⋅  ⋅  ⋅  ⋅  ⋅  1  ⋅  ⋅
@@ -35,7 +35,7 @@
 3×11 Matrix{Int64}:
  1  4  13  1  7  1  10  1  4  13  1
  2  5  14  2  8  2  11  2  5  14  2
- 3  6  15  3  9  3  12  3  6  15  3
source
OneHotArrays.OneHotArray — Type
OneHotArray{T, N, M, I} <: AbstractArray{Bool, M}
-OneHotArray(indices, L)

A one-hot M-dimensional array with L labels (i.e. size(A, 1) == L and sum(A, dims=1) == 1) stored as a compact N == M-1-dimensional array of indices.

Typically constructed by onehot and onehotbatch. Parameter I is the type of the underlying storage, and T its eltype.

source
OneHotArrays.OneHotMatrix — Type
OneHotMatrix{T, I} = OneHotArray{T, 1, 2, I}
-OneHotMatrix(indices, L)

A one-hot matrix (with L labels) typically constructed using onehotbatch. Stored efficiently as a vector of indices with type I and eltype T.

source
OneHotArrays.OneHotVector — Type
OneHotVector{T} = OneHotArray{T, 0, 1, T}
-OneHotVector(indices, L)

A one-hot vector with L labels (i.e. length(A) == L and count(A) == 1) typically constructed by onehot. Stored efficiently as a single index of type T, usually UInt32.

source
+ 3 6 15 3 9 3 12 3 6 15 3source
OneHotArrays.OneHotArray — Type
OneHotArray{T, N, M, I} <: AbstractArray{Bool, M}
+OneHotArray(indices, L)

A one-hot M-dimensional array with L labels (i.e. size(A, 1) == L and sum(A, dims=1) == 1) stored as a compact N == M-1-dimensional array of indices.

Typically constructed by onehot and onehotbatch. Parameter I is the type of the underlying storage, and T its eltype.

source
OneHotArrays.OneHotMatrix — Type
OneHotMatrix{T, I} = OneHotArray{T, 1, 2, I}
+OneHotMatrix(indices, L)

A one-hot matrix (with L labels) typically constructed using onehotbatch. Stored efficiently as a vector of indices with type I and eltype T.

source
OneHotArrays.OneHotVector — Type
OneHotVector{T} = OneHotArray{T, 0, 1, T}
+OneHotVector(indices, L)

A one-hot vector with L labels (i.e. length(A) == L and count(A) == 1) typically constructed by onehot. Stored efficiently as a single index of type T, usually UInt32.

source
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