refactor imports

src/RedClust.jl

@@ -3,49 +3,67 @@
 
 ## Introduction
 
-RedClust is the main module of `RedClust.jl`, a Julia package for Bayesian clustering of high-dimensional Euclidean data using pairwise dissimilarities instead of the raw observations. 
+RedClust is the main module of `RedClust.jl`, a Julia package for Bayesian clustering of high-dimensional Euclidean data using pairwise dissimilarities instead of the raw observations.
 
-Use `names(RedClust)` to get the export list of this module, and type `?name` to get help on a specific `name`. 
+Use `names(RedClust)` to get the export list of this module, and type `?name` to get help on a specific `name`.
 
 See https://abhinavnatarajan.github.io/RedClust.jl/ for detailed documentation.
 """
 module RedClust
 
-export 
-# fit prior
-fitprior, 
-fitprior2,
-sampledist,
-sampleK,
-
-# MCMC sampler
-runsampler,
-
-# utility functions
-adjacencymatrix, 
-sortlabels, 
-uppertriangle, 
-generatemixture, 
-makematrix, 
-
-# summary functions
-evaluateclustering, 
-summarise,
-
-# point estimate
-getpointestimate,
-binderloss,
-infodist, 
-
-# types
-MCMCOptionsList, 
-PriorHyperparamsList, 
-MCMCData, 
-MCMCResult,
-
-# data
-example_datasets, 
-example_dataset
+# mcmc.jl
+using Clustering: kmeans, kmedoids, mutualinfo, randindex, varinfo, vmeasure
+using Dates
+using Distances: Euclidean, pairwise
+using Distributions: Beta, Dirichlet, Distributions, Gamma, MvNormal, Normal, Uniform,
+    fit_mle, logpdf, pdf, rate, shape, truncated
+using HDF5: close, create_group, h5open
+using LinearAlgebra: Diagonal, I
+using LoopVectorization: @turbo
+using Printf: @sprintf
+using ProgressBars: ProgressBar
+using Random: AbstractRNG, TaskLocalRNG, rand, seed!
+using SpecialFunctions: logbeta, loggamma
+using StaticArrays
+using StatsBase:
+    autocor, counts, entropy, levelsmap, mean, mean_and_var, sample, std, var, wsample
+
+
+export
+    # fit prior
+    fitprior,
+    fitprior2,
+    sampledist,
+    sampleK,
+
+    # MCMC sampler
+    runsampler,
+
+    # utility functions
+    adjacencymatrix,
+    sortlabels,
+    uppertriangle,
+    generatemixture,
+    makematrix,
+
+    # summary functions
+    evaluateclustering,
+    summarise,
+
+    # point estimate
+    getpointestimate,
+    binderloss,
+    infodist,
+
+    # types
+    MCMCOptionsList,
+    PriorHyperparamsList,
+    MCMCData,
+    MCMCResult,
+
+    # data
+    example_datasets,
+    example_dataset
 
 include("./types.jl")
 include("./utils.jl")
@@ -55,4 +73,4 @@ include("./pointestimate.jl")
 include("./summaries.jl")
 include("./example_data.jl")
 
-end
+end

src/example_data.jl

@@ -1,11 +1,8 @@
-using Random: seed!
-using HDF5: h5open, close, create_group
-
 const datafilename = joinpath(@__DIR__, "..", "data", "example_datasets.h5")
 
 # function generate_example_data(n::Int)
-#     seed!(44) 
-#     K = 10 # Number of clusters 
+#     seed!(44)
+#     K = 10 # Number of clusters
 #     N = 100 # Number of points
 #     data_σ = [0.25, 0.2, 0.18][n] # Variance of the normal kernel
 #     data_dim = [10, 50, 10][n] # Data dimension
@@ -24,51 +21,51 @@ const datafilename = joinpath(@__DIR__, "..", "data", "example_datasets.h5")
 #         example["cluster_labels"] = clusts
 #         example["cluster_weights"] = probs
 #         example["oracle_coclustering_probabilities"] = oracle_coclustering
-#     end    
+#     end
 #     close(datafile)
 # end
 
 # save_example_data()
 
 """
-Return a read-only handle to a HDF5 file that contains example datasets from the main paper. You must remember to close the file once you are done reading from it. This function is provided for reproducibility purposes only; it is recommended to read the datasets via the convenience function [`example_dataset`](@ref). 
+Return a read-only handle to a HDF5 file that contains example datasets from the main paper. You must remember to close the file once you are done reading from it. This function is provided for reproducibility purposes only; it is recommended to read the datasets via the convenience function [`example_dataset`](@ref).
 """
 function example_datasets()
     h5open(datafilename, "r")
 end
 
 @doc raw"""
-    example_dataset(n::Int)
+	example_dataset(n::Int)
 
 Returns a named tuple containing the dataset from the n``^{\mathrm{th}}`` simulated example in the main paper. This dataset was generated using the following code in Julia v1.8.1:
 ```julia
-    using RedClust
-    using Random: seed!
-    seed!(44)
-    K = 10 # Number of clusters 
-    N = 100 # Number of points
-    data_σ = [0.25, 0.2, 0.18][n] # Variance of the normal kernel
-    data_dim = [10, 50, 10][n] # Data dimension
-    data = generatemixture(N, K; α = 10, σ = data_σ, dim = data_dim);
+	using RedClust
+	using Random: seed!
+	seed!(44)
+	K = 10 # Number of clusters
+	N = 100 # Number of points
+	data_σ = [0.25, 0.2, 0.18][n] # Variance of the normal kernel
+	data_dim = [10, 50, 10][n] # Data dimension
+	data = generatemixture(N, K; α = 10, σ = data_σ, dim = data_dim);
 ```
 
 Note however that the above code may produce different results on your computer because the random number generator in Julia is not meant for reproducible results across different computers, different versions of Julia, or different versions of the Random.jl package, even with appropriate seeding. Therefore the datasets have been included with this package, and it is recommended to access them via this function.
 
-See also [`generatemixture`](@ref). 
+See also [`generatemixture`](@ref).
 """
 function example_dataset(n::Int)
     if n ∉ [1, 2, 3]
         throw(ArgumentError("n must be 1, 2, or 3."))
     end
     datafile = example_datasets()
-    egdata = datafile["example" * string(n)]
+    egdata = datafile["example"*string(n)]
     x = read(egdata["points"])
     points = [x[:, i] for i in 1:last(size(x))]
     clusts = read(egdata["cluster_labels"])
     distmatrix = read(egdata["distance_matrix"])
     probs = read(egdata["cluster_weights"])
     oracle_coclustering = read(egdata["oracle_coclustering_probabilities"])
     close(datafile)
-    data = (points = points, distmatrix = distmatrix, clusts = clusts, probs = probs, oracle_coclustering = oracle_coclustering)
+    data = (points=points, distmatrix=distmatrix, clusts=clusts, probs=probs, oracle_coclustering=oracle_coclustering)
     return data
-end
+end