Merge pull request #85 from AP6YC/release/v0.6.0

v0.6.0

.gitattributes

@@ -0,0 +1,7 @@
+# -----------------------------------------------------------------------------
+# LFS Settings
+# -----------------------------------------------------------------------------
+
+# LFS images
+*.png filter=lfs diff=lfs merge=lfs -text
+*.ico filter=lfs diff=lfs merge=lfs -text

.github/workflows/CompatHelper.yml

@@ -13,4 +13,4 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           COMPATHELPER_PRIV: ${{ secrets.COMPATHELPER_PRIV }}  # optional
-        run: julia -e 'using CompatHelper; CompatHelper.main()'
+        run: julia -e 'using CompatHelper; CompatHelper.main()'

.github/workflows/Documentation.yml

@@ -20,14 +20,16 @@ jobs:
           access_token: ${{ github.token }}
 
       - uses: actions/checkout@v2
+        with:
+          lfs: 'true'
       - uses: julia-actions/setup-julia@latest
         with:
-          version: '1.6'
+          version: '1.8'
       - name: Install dependencies
         run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
       - name: Build and deploy
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
           DATADEPS_ALWAYS_ACCEPT: true
           # DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # For authentication with SSH deploy key
-        run: julia --project=docs/ docs/make.jl
+        run: julia --project=docs/ docs/make.jl

.gitignore

@@ -1,3 +1,13 @@
+# IDE ignores
+.vscode/
+
+# Project ignores
+_dev/
+
+# DemoCards intermediate compilation directories
+docs/src/democards/
+docs/src/examples/
+
 # Julia package development ignores
 *.jl.*.cov
 *.jl.cov
@@ -8,10 +18,3 @@
 /docs/Manifest.toml
 /docs/build/
 /docs/site/
-
-# Project ignores
-data/mnist/
-!data/.gitkeep
-
-# IDE ignores
-.vscode/

CONTRIBUTING.md

@@ -0,0 +1,10 @@
+# Contributing to the Project
+
+The `AdaptiveResonance.jl` project is open sourced and open to contribution!
+If you would like to contribute to the project, you should know:
+
+1. The [README](README.md) contains a quick summary of the contributing guidelines.
+2. The [code of conduct](CODE_OF_CONDUCT.md) declares the behavior guidelines for contributors, mainly concerning respect and decorum.
+3. The [contributing guide](https://ap6yc.github.io/AdaptiveResonance.jl/dev/man/contributing/) in the official documentation provides an in-depth guide to contributing, such as how to do GitFlow, how to develop in Julia, and how the internals of the package work.
+
+In summary, the main point of contact is the [GitHub issues](https://github.com/AP6YC/AdaptiveResonance.jl/issues), and code development follows the GitFlow paradigm (i.e., create a `feature/...` branch off of the latest `develop` and submit a pull request back into `develop`).

Project.toml

@@ -2,21 +2,22 @@ name = "AdaptiveResonance"
 uuid = "3d72adc0-63d3-4141-bf9b-84450dd0395b"
 authors = ["Sasha Petrenko"]
 description = "A Julia package for Adaptive Resonance Theory (ART) algorithms."
-version = "0.5.1"
+version = "0.6.0"
 
 [deps]
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
+NumericalTypeAliases = "be9b823e-291e-41a1-b8ce-806204e78f92"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
-Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
 SharedArrays = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 DocStringExtensions = "0.8, 0.9"
+NumericalTypeAliases = "0.1, 0.2"
 Parameters = "0.12"
 ProgressBars = "0.7, 0.8, 1"
 julia = "1"

README.md

@@ -1,7 +1,6 @@
-# AdaptiveResonance
+[![adaptiveresonance-header](docs/src/assets/header.png)][docs-dev-url]
 
 A Julia package for Adaptive Resonance Theory (ART) algorithms.
-d
 
 | **Documentation**  | **Testing Status** | **Coverage** | **Reference** |
 |:------------------:|:----------------:|:------------:|:-------------:|
@@ -53,20 +52,19 @@ Please read the [documentation](https://ap6yc.github.io/AdaptiveResonance.jl/dev
 
 ## Contents
 
-- [AdaptiveResonance](#adaptiveresonance)
-  - [Contents](#contents)
-  - [Overview](#overview)
-  - [Contributing](#contributing)
-  - [Installation](#installation)
-  - [Quickstart](#quickstart)
-  - [Implemented Modules](#implemented-modules)
-  - [Structure](#structure)
-  - [History](#history)
-  - [Acknowledgements](#acknowledgements)
-    - [Authors](#authors)
-    - [Software](#software)
-    - [Datasets](#datasets)
-  - [License](#license)
+- [Contents](#contents)
+- [Overview](#overview)
+- [Contributing](#contributing)
+- [Installation](#installation)
+- [Quickstart](#quickstart)
+- [Implemented Modules](#implemented-modules)
+- [Structure](#structure)
+- [History](#history)
+- [Acknowledgements](#acknowledgements)
+  - [Authors](#authors)
+  - [Software](#software)
+  - [Datasets](#datasets)
+- [License](#license)
 
 ## Overview
 
@@ -96,21 +94,23 @@ Patch versions are for bug fixes, minor versions are for backward-compatible cha
 This project is distributed as a Julia package, available on [JuliaHub](https://juliahub.com/).
 Its usage follows the usual Julia package installation procedure, interactively:
 
-```julia
-] add AdaptiveResonance
+```julia-repl
+julia> ]
+(@v1.8) pkg> add AdaptiveResonance
 ```
 
 or programmatically:
 
-```julia
-using Pkg
-Pkg.add("AdaptiveResonance")
+```julia-repl
+julia> using Pkg
+julia> Pkg.add("AdaptiveResonance")
 ```
 
 You may also add the package directly from GitHub to get the latest changes between releases:
 
-```julia
-] add https://github.com/AP6YC/AdaptiveResonance.jl
+```julia-repl
+julia> ]
+(@v1.8) pkg> add https://github.com/AP6YC/AdaptiveResonance.jl
 ```
 
 ## Quickstart
@@ -209,16 +209,19 @@ The following file tree summarizes the project structure:
 ```console
 AdaptiveResonance
 ├── .github/workflows       // GitHub: workflows for testing and documentation.
-├── data                    // Data: CI data location.
 ├── docs                    // Docs: documentation for the module.
-│   ├─── examples          //      DemoCards documentation examples.
-│   └─── src               //      Documentation source files.
+│   ├─── examples           //      DemoCards documentation examples.
+│   └─── src                //      Documentation source files.
+├── paper                   // JOSS: journal paper and citations.
 ├── src                     // Source: majority of source code.
-│   ├─── ART               //      ART-based unsupervised modules.
-│   └─── ARTMAP            //      ARTMAP-based supervised modules.
+│   ├─── ART                //      ART-based unsupervised modules.
+│   └─── ARTMAP             //      ARTMAP-based supervised modules.
 ├── test                    // Test: Unit, integration, and environment tests.
 ├── .appveyor               // Appveyor: Windows-specific coverage.
+├── .gitattributes          // Git: LFS settings, languages, etc.
 ├── .gitignore              // Git: .gitignore for the whole project.
+├── CODE_OF_CONDUCT.md      // Doc: the code of conduct for contributors.
+├── CONTRIBUTING.md         // Doc: contributing guide (points to this page).
 ├── LICENSE                 // Doc: the license to the project.
 ├── Project.toml            // Julia: the Pkg.jl dependencies of the project.
 └── README.md               // Doc: this document.

docs/Project.toml

@@ -1,5 +1,6 @@
 [deps]
 AdaptiveResonance = "3d72adc0-63d3-4141-bf9b-84450dd0395b"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DemoCards = "311a05b2-6137-4a5a-b473-18580a3d38b5"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
@@ -10,4 +11,4 @@ MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 
 [compat]
-MLDatasets = "0.6"
+MLDatasets = "0.7"

docs/examples/adaptive_resonance/data_config.jl

@@ -4,7 +4,7 @@
 # cover: ../assets/art.png
 # date: 2021-12-2
 # author: "[Sasha Petrenko](https://github.com/AP6YC)"
-# julia: 1.6
+# julia: 1.8
 # description: This demo illustrates how the data configuration object works for data preprocessing in ART modules that require it.
 # ---
 
@@ -46,16 +46,17 @@ fieldnames(AdaptiveResonance.DataConfig)
 
 ## Load data
 using MLDatasets        # Iris dataset
+using DataFrames        # DataFrames, necessary for MLDatasets.Iris()
 using MLDataUtils       # Shuffling and splitting
 
-## We will download the Iris dataset for its small size and benchmark use for clustering algorithms.
-## Get the iris dataset as a DataFrame
-iris = Iris()
+# We will download the Iris dataset for its small size and benchmark use for clustering algorithms.
+## Get the iris dataset
+iris = Iris(as_df=false)
 ## Manipulate the features and labels into a matrix of features and a vector of labels
-features, labels = Matrix(iris.features)', vec(Matrix{String}(iris.targets))
+features, labels = iris.features, iris.targets
 
 # Because the MLDatasets package gives us Iris labels as strings, we will use the `MLDataUtils.convertlabel` method with the `MLLabelUtils.LabelEnc.Indices` type to get a list of integers representing each class:
-labels = convertlabel(LabelEnc.Indices{Int}, labels)
+labels = convertlabel(LabelEnc.Indices{Int}, vec(labels))
 unique(labels)
 
 # !!! note

docs/examples/adaptive_resonance/incremental-batch.jl

@@ -4,7 +4,7 @@
 # cover: assets/incremental-batch-cover.png
 # date: 2021-12-1
 # author: "[Sasha Petrenko](https://github.com/AP6YC)"
-# julia: 1.6
+# julia: 1.8
 # description: This demo illustrates how to use incremental training methods vs. batch training for all ART modules.
 # ---
 
@@ -23,17 +23,18 @@
 # We begin with importing AdaptiveResonance for the ART modules and MLDatasets for some data utilities.
 using AdaptiveResonance # ART
 using MLDatasets        # Iris dataset
+using DataFrames        # DataFrames, necessary for MLDatasets.Iris()
 using MLDataUtils       # Shuffling and splitting
 using Printf            # Formatted number printing
 
 # We will download the Iris dataset for its small size and benchmark use for clustering algorithms.
-## Get the iris dataset as a DataFrame
-iris = Iris()
+## Get the iris dataset
+iris = Iris(as_df=false)
 ## Manipulate the features and labels into a matrix of features and a vector of labels
-features, labels = Matrix(iris.features)', vec(Matrix{String}(iris.targets))
+features, labels = iris.features, iris.targets
 
 # Because the MLDatasets package gives us Iris labels as strings, we will use the `MLDataUtils.convertlabel` method with the `MLLabelUtils.LabelEnc.Indices` type to get a list of integers representing each class:
-labels = convertlabel(LabelEnc.Indices{Int}, labels)
+labels = convertlabel(LabelEnc.Indices{Int}, vec(labels))
 unique(labels)
 
 # Next, we will create a train/test split with the `MLDataUtils.stratifiedobs` utility:
@@ -73,7 +74,7 @@ n_train = length(y_train)
 ## Create a container for the training output labels
 y_hat_incremental_train = zeros(Int, n_train)
 ## Iterate over all training samples
-for ix = 1:length(y_train)
+for ix in eachindex(y_train)
     sample = X_train[:, ix]
     label = y_train[ix]
     y_hat_incremental_train[ix] = train!(art_incremental, sample, y=label)
@@ -128,12 +129,13 @@ perf_test_incremental = performance(y_hat_incremental, y_test)
 using Printf            # Formatted number printing
 using MultivariateStats # Principal component analysis (PCA)
 using Plots             # Plotting frontend
+gr()                    # Use the default GR backend explicitly
 
 ## Train a PCA model
 M = fit(PCA, features; maxoutdim=2)
 
 ## Apply the PCA model to the testing set
-X_test_pca = transform(M, X_test)
+X_test_pca = MultivariateStats.transform(M, X_test)
 
 # Now that we have the test points cast into a 2-D set of points, we can create a scatter plot that shows how each point is categorized by the modules.
 

docs/examples/adaptive_resonance/options.jl

@@ -4,7 +4,7 @@
 # cover: assets/options-cover.png
 # date: 2021-12-2
 # author: "[Sasha Petrenko](https://github.com/AP6YC)"
-# julia: 1.6
+# julia: 1.8
 # description: This demo illustrates how to use options and modify the options for all ART and ARTMAP modules.
 # ---
 
@@ -88,19 +88,21 @@ my_fuzzyart.opts.rho=0.6
 
 # We begin with importing AdaptiveResonance for the ART modules and MLDatasets for some data utilities.
 using MLDatasets        # Iris dataset
+using DataFrames        # DataFrames, necessary for MLDatasets.Iris()
 using MLDataUtils       # Shuffling and splitting
 using Printf            # Formatted number printing
 using MultivariateStats # Principal component analysis (PCA)
 using Plots             # Plotting frontend
+gr()                    # Use the default GR backend explicitly
 
 # We will download the Iris dataset for its small size and benchmark use for clustering algorithms.
-## Get the iris dataset as a DataFrame
-iris = Iris()
+## Get the iris dataset
+iris = Iris(as_df=false)
 ## Manipulate the features and labels into a matrix of features and a vector of labels
-features, labels = Matrix(iris.features)', vec(Matrix{String}(iris.targets))
+features, labels = iris.features, iris.targets
 
 # Because the MLDatasets package gives us Iris labels as strings, we will use the `MLDataUtils.convertlabel` method with the `MLLabelUtils.LabelEnc.Indices` type to get a list of integers representing each class:
-labels = convertlabel(LabelEnc.Indices{Int}, labels)
+labels = convertlabel(LabelEnc.Indices{Int}, vec(labels))
 unique(labels)
 
 # Next, we will create a train/test split with the `MLDataUtils.stratifiedobs` utility:
@@ -152,7 +154,7 @@ perf_test_2 = performance(y_hat_2, y_test)
 M = fit(PCA, features; maxoutdim=2)
 
 ## Apply the PCA model to the testing set
-X_test_pca = transform(M, X_test)
+X_test_pca = MultivariateStats.transform(M, X_test)
 
 # We can now plot the PCA'ed test set and label them according to the two FuzzyART's
 # We will do so by creating a function for the subplots first as they will share the same format, and we dare not duplicate code.

docs/examples/art/ddvfa_supervised.jl

@@ -4,7 +4,7 @@
 # cover: ../assets/ddvfa.png
 # date: 2021-11-30
 # author: "[Sasha Petrenko](https://github.com/AP6YC)"
-# julia: 1.6
+# julia: 1.8
 # description: This demo shows how to use DDVFA for simple supervised learning by clustering Iris samples and mapping the modules internal categories to the true labels.
 # ---
 
@@ -14,17 +14,18 @@
 # We begin with importing AdaptiveResonance for the ART modules and MLDatasets for some data utilities.
 using AdaptiveResonance # ART
 using MLDatasets        # Iris dataset
+using DataFrames        # DataFrames, necessary for MLDatasets.Iris()
 using MLDataUtils       # Shuffling and splitting
 using Printf            # Formatted number printing
 
 # We will download the Iris dataset for its small size and benchmark use for clustering algorithms.
-## Get the iris dataset as a DataFrame
-iris = Iris()
+## Get the iris dataset
+iris = Iris(as_df=false)
 ## Manipulate the features and labels into a matrix of features and a vector of labels
-features, labels = Matrix(iris.features)', vec(Matrix{String}(iris.targets))
+features, labels = iris.features, iris.targets
 
 # Because the MLDatasets package gives us Iris labels as strings, we will use the `MLDataUtils.convertlabel` method with the `MLLabelUtils.LabelEnc.Indices` type to get a list of integers representing each class:
-labels = convertlabel(LabelEnc.Indices{Int}, labels)
+labels = convertlabel(LabelEnc.Indices{Int}, vec(labels))
 unique(labels)
 
 # Next, we will create a train/test split with the `MLDataUtils.stratifiedobs` utility: