build based on 8cc46c8

stable

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-v0.1.6
+v0.1.7

v0.1

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-v0.1.6
+v0.1.7

v0.1.7/.documenter-siteinfo.json

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+{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-09T13:59:40","documenter_version":"1.7.0"}}

v0.1.7/LICENSE

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+MIT License
+
+Copyright (c) 2023 Patrick Altmeyer
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

v0.1.7/_intro.qmd

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+*Joint Energy Models in Julia.*
+
+[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliatrustworthyai.github.io/JointEnergyModels.jl/stable)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliatrustworthyai.github.io/JointEnergyModels.jl/dev)
+[![Build Status](https://github.com/juliatrustworthyai/JointEnergyModels.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/juliatrustworthyai/JointEnergyModels.jl/actions/workflows/CI.yml?query=branch%3Amain)
+[![Coverage](https://codecov.io/gh/juliatrustworthyai/JointEnergyModels.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/juliatrustworthyai/JointEnergyModels.jl)
+[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
+[![License](https://img.shields.io/github/license/juliatrustworthyai/JointEnergyModels.jl)](LICENSE)
+[![Package Downloads](https://img.shields.io/badge/dynamic/json?url=http%3A%2F%2Fjuliapkgstats.com%2Fapi%2Fv1%2Fmonthly_downloads%2FJointEnergyModels&query=total_requests&suffix=%2Fmonth&label=Downloads)](http://juliapkgstats.com/pkg/JointEnergyModels) 
+[![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)
+
+```{julia}
+#| echo: false
+
+include("$(pwd())/docs/setup_docs.jl")
+eval(setup_docs)
+```
+
+`JointEnergyModels.jl` is a package for training Joint Energy Models in Julia. Joint Energy Models (JEM) are hybrid models that learn to discriminate between classes $y$ and generate input data $x$. They were introduced in @grathwohl2020your, which provides the foundation for the methodologies implemented in this package.
+
+## 🔁 Status
+
+This package is still in its infancy and the API is subject to change. Currently, the package can be used to train JEMs for classification. It is also possible to train pure Energy-Based Models (EBMs) for the generative task only. The package is compatible with `Flux.jl`. Work on compatibility with `MLJ.jl` (through `MLJFlux.jl`) is currently under way.
+
+We welcome contributions and feedback at this early stage. To install the development version of the package you can run the following command:
+
+```{.julia}
+using Pkg
+Pkg.add(url="https://github.com/juliatrustworthyai/JointEnergyModels.jl")
+```
+
+## 🔍 Usage Example
+
+```{=commonmark}
+!!! warning "Breaking Changes Anticipated"
+    To facilitate the interface to MLJFlux, this package currently overloads private methods. We are still deliberating 
+```
+
+Below we first generate some synthetic data:
+
+```{julia}
+#| output: true
+
+nobs=2000
+X, y = make_circles(nobs, noise=0.1, factor=0.5)
+Xplot = Float32.(permutedims(matrix(X)))
+X = table(permutedims(Xplot))
+plt = scatter(Xplot[1,:], Xplot[2,:], group=y, label="")
+batch_size = Int(round(nobs/10))
+```
+
+The `MLJFlux` compatible classifier can be instantiated as follows:
+
+```{julia}
+𝒟x = Normal()
+𝒟y = Categorical(ones(2) ./ 2)
+sampler = ConditionalSampler(𝒟x, 𝒟y, input_size=size(Xplot)[1:end-1], batch_size=batch_size)
+clf = JointEnergyClassifier(
+    sampler;
+    builder=MLJFlux.MLP(hidden=(32, 32, 32,), σ=Flux.relu),
+    batch_size=batch_size,
+    finaliser=x -> x,
+    loss=Flux.Losses.logitcrossentropy,
+)
+```
+
+It uses the `MLJFlux` package to build the model:
+
+```{julia}
+#| output: true
+
+println(typeof(clf) <: MLJFlux.MLJFluxModel)
+```
+
+The model can be wrapped in data and trained using the `fit!` function:
+
+```{julia}
+mach = machine(clf, X, y)
+fit!(mach, verbosity=1)
+```
+
+The results are visualised below. The model has learned to discriminate between the two classes (as indicated by the contours) and to generate samples from each class (as indicated by the stars).
+
+```{julia}
+#| echo: false
+
+jem = mach.model.jem
+batch_size = mach.model.batch_size
+X = Float32.(permutedims(matrix(X)))
+y_labels = Int.(y.refs)
+y = Flux.onehotbatch(y.refs, sort(unique(y_labels)))
+```
+
+```{julia}
+#| output: true
+#| echo: false
+
+if typeof(jem.sampler) <: ConditionalSampler
+    
+    plts = []
+    for target in 1:size(y,1)
+        X̂ = generate_conditional_samples(jem, batch_size, target; niter=1000) 
+        ex = extrema(hcat(X,X̂), dims=2)
+        xlims = ex[1]
+        ylims = ex[2]
+        x1 = range(1.0f0.*xlims...,length=100)
+        x2 = range(1.0f0.*ylims...,length=100)
+        plt = contour(
+            x1, x2, (x, y) -> softmax(jem([x, y]))[target], 
+            fill=true, alpha=0.5, title="Target: $target", cbar=true,
+            xlims=xlims,
+            ylims=ylims,
+        )
+        scatter!(X[1,:], X[2,:], color=vec(y_labels), group=vec(y_labels), alpha=0.5)
+        scatter!(
+            X̂[1,:], X̂[2,:], 
+            color=repeat([target], size(X̂,2)), 
+            group=repeat([target], size(X̂,2)), 
+            shape=:star5, ms=10
+        )
+        push!(plts, plt)
+    end
+    plt = plot(plts..., layout=(1, size(y,1)), size=(size(y,1)*500, 400))
+    display(plt)
+end
+```
+
+## 🎓 References

v0.1.7/_metadata.yml

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+format: 
+  commonmark:
+    variant: -raw_html+tex_math_dollars
+    wrap: none
+    mermaid-format: png
+bibliography: ../../bib.bib