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julia> Flux.onecold([0.3, 0.2, 0.5], (:a, :b, :c))
:c</code></pre><p>In addition to functions for encoding and decoding data as one-hot, this package provides numerous &quot;fast-paths&quot; 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.</p></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="reference/">Reference »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Saturday 16 November 2024 16:14">Saturday 16 November 2024</span>. Using Julia version 1.6.7.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
:c</code></pre><p>In addition to functions for encoding and decoding data as one-hot, this package provides numerous &quot;fast-paths&quot; 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.</p></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="reference/">Reference »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Saturday 16 November 2024 17:17">Saturday 16 November 2024</span>. Using Julia version 1.6.7.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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0 0 1 0 0 0 0 0 0 1 0 ], &#39;a&#39;:&#39;e&#39;) |&gt; String
&quot;abeacadabea&quot;</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/96f04c8008518df1d7b9bccb3a31d89f00e96885/src/onehot.jl#L123-L148">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="OneHotArrays.onehot-Tuple{Any, Any}" href="#OneHotArrays.onehot-Tuple{Any, Any}"><code>OneHotArrays.onehot</code></a><span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">onehot(x, labels, [default])</code></pre><p>Returns a <a href="#OneHotArrays.OneHotVector"><code>OneHotVector</code></a> which is roughly a sparse representation of <code>x .== labels</code>.</p><p>Instead of storing say <code>Vector{Bool}</code>, it stores the index of the first occurrence of <code>x</code> in <code>labels</code>. If <code>x</code> is not found in labels, then it either returns <code>onehot(default, labels)</code>, or gives an error if no default is given.</p><p>See also <a href="#OneHotArrays.onehotbatch-Tuple{Any, Any, Vararg{Any, N} where N}"><code>onehotbatch</code></a> to apply this to many <code>x</code>s, and <a href="#OneHotArrays.onecold"><code>onecold</code></a> to reverse either of these, as well as to generalise <code>argmax</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl hljs">julia&gt; β = onehot(:b, (:a, :b, :c))
&quot;abeacadabea&quot;</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/82c8ed42df68f8f52aee7e91f3c97a60fabdd6d7/src/onehot.jl#L123-L148">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="OneHotArrays.onehot-Tuple{Any, Any}" href="#OneHotArrays.onehot-Tuple{Any, Any}"><code>OneHotArrays.onehot</code></a><span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">onehot(x, labels, [default])</code></pre><p>Returns a <a href="#OneHotArrays.OneHotVector"><code>OneHotVector</code></a> which is roughly a sparse representation of <code>x .== labels</code>.</p><p>Instead of storing say <code>Vector{Bool}</code>, it stores the index of the first occurrence of <code>x</code> in <code>labels</code>. If <code>x</code> is not found in labels, then it either returns <code>onehot(default, labels)</code>, or gives an error if no default is given.</p><p>See also <a href="#OneHotArrays.onehotbatch-Tuple{Any, Any, Vararg{Any, N} where N}"><code>onehotbatch</code></a> to apply this to many <code>x</code>s, and <a href="#OneHotArrays.onecold"><code>onecold</code></a> to reverse either of these, as well as to generalise <code>argmax</code>.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl hljs">julia&gt; 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</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/96f04c8008518df1d7b9bccb3a31d89f00e96885/src/onehot.jl#L1-L30">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="OneHotArrays.onehotbatch-Tuple{Any, Any, Vararg{Any, N} where N}" href="#OneHotArrays.onehotbatch-Tuple{Any, Any, Vararg{Any, N} where N}"><code>OneHotArrays.onehotbatch</code></a><span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">onehotbatch(xs, labels, [default])</code></pre><p>Returns a <a href="#OneHotArrays.OneHotMatrix"><code>OneHotMatrix</code></a> where <code>k</code>th column of the matrix is <a href="#OneHotArrays.onehot-Tuple{Any, Any}"><code>onehot(xs[k], labels)</code></a>. This is a sparse matrix, which stores just a <code>Vector{UInt32}</code> containing the indices of the nonzero elements.</p><p>If one of the inputs in <code>xs</code> is not found in <code>labels</code>, that column is <code>onehot(default, labels)</code> if <code>default</code> is given, else an error.</p><p>If <code>xs</code> has more dimensions, <code>N = ndims(xs) &gt; 1</code>, then the result is an <code>AbstractArray{Bool, N+1}</code> which is one-hot along the first dimension, i.e. <code>result[:, k...] == onehot(xs[k...], labels)</code>.</p><p>Note that <code>xs</code> can be any iterable, such as a string. And that using a tuple for <code>labels</code> will often speed up construction, certainly for less than 32 classes.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl hljs">julia&gt; oh = onehotbatch(&quot;abracadabra&quot;, &#39;a&#39;:&#39;e&#39;, &#39;e&#39;)
⋅ ⋅ 1</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/82c8ed42df68f8f52aee7e91f3c97a60fabdd6d7/src/onehot.jl#L1-L30">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="OneHotArrays.onehotbatch-Tuple{Any, Any, Vararg{Any, N} where N}" href="#OneHotArrays.onehotbatch-Tuple{Any, Any, Vararg{Any, N} where N}"><code>OneHotArrays.onehotbatch</code></a><span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">onehotbatch(xs, labels, [default])</code></pre><p>Returns a <a href="#OneHotArrays.OneHotMatrix"><code>OneHotMatrix</code></a> where <code>k</code>th column of the matrix is <a href="#OneHotArrays.onehot-Tuple{Any, Any}"><code>onehot(xs[k], labels)</code></a>. This is a sparse matrix, which stores just a <code>Vector{UInt32}</code> containing the indices of the nonzero elements.</p><p>If one of the inputs in <code>xs</code> is not found in <code>labels</code>, that column is <code>onehot(default, labels)</code> if <code>default</code> is given, else an error.</p><p>If <code>xs</code> has more dimensions, <code>N = ndims(xs) &gt; 1</code>, then the result is an <code>AbstractArray{Bool, N+1}</code> which is one-hot along the first dimension, i.e. <code>result[:, k...] == onehot(xs[k...], labels)</code>.</p><p>Note that <code>xs</code> can be any iterable, such as a string. And that using a tuple for <code>labels</code> will often speed up construction, certainly for less than 32 classes.</p><p><strong>Examples</strong></p><pre><code class="language-julia-repl hljs">julia&gt; oh = onehotbatch(&quot;abracadabra&quot;, &#39;a&#39;:&#39;e&#39;, &#39;e&#39;)
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</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/96f04c8008518df1d7b9bccb3a31d89f00e96885/src/onehot.jl#L50-L83">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="OneHotArrays.OneHotArray" href="#OneHotArrays.OneHotArray"><code>OneHotArrays.OneHotArray</code></a><span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">OneHotArray{T, N, M, I} &lt;: AbstractArray{Bool, M}
OneHotArray(indices, L)</code></pre><p>A one-hot <code>M</code>-dimensional array with <code>L</code> labels (i.e. <code>size(A, 1) == L</code> and <code>sum(A, dims=1) == 1</code>) stored as a compact <code>N == M-1</code>-dimensional array of indices.</p><p>Typically constructed by <a href="#OneHotArrays.onehot-Tuple{Any, Any}"><code>onehot</code></a> and <a href="#OneHotArrays.onehotbatch-Tuple{Any, Any, Vararg{Any, N} where N}"><code>onehotbatch</code></a>. Parameter <code>I</code> is the type of the underlying storage, and <code>T</code> its eltype.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/96f04c8008518df1d7b9bccb3a31d89f00e96885/src/array.jl#L1-L10">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="OneHotArrays.OneHotMatrix" href="#OneHotArrays.OneHotMatrix"><code>OneHotArrays.OneHotMatrix</code></a><span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">OneHotMatrix{T, I} = OneHotArray{T, 1, 2, I}
OneHotMatrix(indices, L)</code></pre><p>A one-hot matrix (with <code>L</code> labels) typically constructed using <a href="#OneHotArrays.onehotbatch-Tuple{Any, Any, Vararg{Any, N} where N}"><code>onehotbatch</code></a>. Stored efficiently as a vector of indices with type <code>I</code> and eltype <code>T</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/96f04c8008518df1d7b9bccb3a31d89f00e96885/src/array.jl#L33-L39">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="OneHotArrays.OneHotVector" href="#OneHotArrays.OneHotVector"><code>OneHotArrays.OneHotVector</code></a><span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">OneHotVector{T} = OneHotArray{T, 0, 1, T}
OneHotVector(indices, L)</code></pre><p>A one-hot vector with <code>L</code> labels (i.e. <code>length(A) == L</code> and <code>count(A) == 1</code>) typically constructed by <a href="#OneHotArrays.onehot-Tuple{Any, Any}"><code>onehot</code></a>. Stored efficiently as a single index of type <code>T</code>, usually <code>UInt32</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/96f04c8008518df1d7b9bccb3a31d89f00e96885/src/array.jl#L23-L29">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../">« Overview</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Saturday 16 November 2024 16:14">Saturday 16 November 2024</span>. Using Julia version 1.6.7.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
3 6 15 3 9 3 12 3 6 15 3</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/82c8ed42df68f8f52aee7e91f3c97a60fabdd6d7/src/onehot.jl#L50-L83">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="OneHotArrays.OneHotArray" href="#OneHotArrays.OneHotArray"><code>OneHotArrays.OneHotArray</code></a><span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">OneHotArray{T, N, M, I} &lt;: AbstractArray{Bool, M}
OneHotArray(indices, L)</code></pre><p>A one-hot <code>M</code>-dimensional array with <code>L</code> labels (i.e. <code>size(A, 1) == L</code> and <code>sum(A, dims=1) == 1</code>) stored as a compact <code>N == M-1</code>-dimensional array of indices.</p><p>Typically constructed by <a href="#OneHotArrays.onehot-Tuple{Any, Any}"><code>onehot</code></a> and <a href="#OneHotArrays.onehotbatch-Tuple{Any, Any, Vararg{Any, N} where N}"><code>onehotbatch</code></a>. Parameter <code>I</code> is the type of the underlying storage, and <code>T</code> its eltype.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/82c8ed42df68f8f52aee7e91f3c97a60fabdd6d7/src/array.jl#L1-L10">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="OneHotArrays.OneHotMatrix" href="#OneHotArrays.OneHotMatrix"><code>OneHotArrays.OneHotMatrix</code></a><span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">OneHotMatrix{T, I} = OneHotArray{T, 1, 2, I}
OneHotMatrix(indices, L)</code></pre><p>A one-hot matrix (with <code>L</code> labels) typically constructed using <a href="#OneHotArrays.onehotbatch-Tuple{Any, Any, Vararg{Any, N} where N}"><code>onehotbatch</code></a>. Stored efficiently as a vector of indices with type <code>I</code> and eltype <code>T</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/82c8ed42df68f8f52aee7e91f3c97a60fabdd6d7/src/array.jl#L33-L39">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="OneHotArrays.OneHotVector" href="#OneHotArrays.OneHotVector"><code>OneHotArrays.OneHotVector</code></a><span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">OneHotVector{T} = OneHotArray{T, 0, 1, T}
OneHotVector(indices, L)</code></pre><p>A one-hot vector with <code>L</code> labels (i.e. <code>length(A) == L</code> and <code>count(A) == 1</code>) typically constructed by <a href="#OneHotArrays.onehot-Tuple{Any, Any}"><code>onehot</code></a>. Stored efficiently as a single index of type <code>T</code>, usually <code>UInt32</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/FluxML/OneHotArrays.jl/blob/82c8ed42df68f8f52aee7e91f3c97a60fabdd6d7/src/array.jl#L23-L29">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../">« Overview</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Saturday 16 November 2024 17:17">Saturday 16 November 2024</span>. Using Julia version 1.6.7.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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