Use Clustering.jl instead of ParallelKMeans

JuliaAI · May 15, 2024 · 0acc876 · 0acc876
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Showing 2 changed files with 1 addition and 2 deletions.
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -15,7 +15,6 @@ MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
 MLJMultivariateStatsInterface = "1b6a4a23-ba22-4f51-9698-8599985d3728"
 Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
 NearestNeighborModels = "636a865e-7cf4-491e-846c-de09b730eb36"
-ParallelKMeans = "42b8e9d4-006b-409a-8472-7f34b3fb58af"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ScientificTypesBase = "30f210dd-8aff-4c5f-94ba-8e64358c1161"
 StatisticalMeasures = "a19d573c-0a75-4610-95b3-7071388c7541"

diff --git a/docs/src/transformers.md b/docs/src/transformers.md
@@ -202,7 +202,7 @@ import Random.seed!
 seed!(123)
 
 X, y = @load_iris
-KMeans = @load KMeans pkg=ParallelKMeans
+KMeans = @load KMeans pkg=Clustering
 kmeans = KMeans()
 mach = machine(kmeans, X) |> fit!
 nothing # hide