add compathelper

.github/workflows/CompatHelper.yml

@@ -0,0 +1,32 @@
+name: CompatHelper
+on:
+  schedule:
+    - cron: 0 0 * * *
+  workflow_dispatch:
+jobs:
+  CompatHelper:
+    runs-on: ubuntu-latest
+    steps:
+      - name: "Add the General registry via Git"
+        run: |
+          import Pkg
+          ENV["JULIA_PKG_SERVER"] = ""
+          Pkg.Registry.add("General")
+        shell: julia --color=yes {0}
+      - name: "Install CompatHelper"
+        run: |
+          import Pkg
+          name = "CompatHelper"
+          uuid = "aa819f21-2bde-4658-8897-bab36330d9b7"
+          version = "3"
+          Pkg.add(; name, uuid, version)
+        shell: julia --color=yes {0}
+      - name: "Run CompatHelper"
+        run: |
+          import CompatHelper
+          CompatHelper.main()
+        shell: julia --color=yes {0}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}
+          # COMPATHELPER_PRIV: ${{ secrets.COMPATHELPER_PRIV }}

Project.toml

@@ -10,7 +10,7 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
-julia = "1.6"
+julia = "1"
 LinearAlgebra = "1.6"
 NearestNeighbors = "0.4.9"
 Statistics = "1.4.0"

test/dbscan.jl

@@ -3,14 +3,22 @@
     Random.seed!(42)
 
     df = CSV.read("data/blob_data.csv", DataFrame, drop=[1]);
-
     X = df[:,1:2];
+
     ϵ = 0.35;
     min_pts = 10;
 
-    X_int = round.(Int, X.*100)
-    ϵ_int = 35.0;
+    @testset "test errors" begin
+        @test_throws ArgumentError dbscan(X[:, 2], ϵ, min_pts)
+    end
 
+    @testset "test Array{Float64, 2}" begin 
+        X_array = Matrix(X)
+        res_ar = dbscan(X_array, ϵ, min_pts)
+        n_clusters = unique(res_ar.labels) |> length
+        @test length(res_ar.labels) == size(X_array, 1)
+    end
+
     @testset "test dimensions results" begin 
         res = dbscan(X, ϵ, min_pts)
         n_clusters = unique(res.labels) |> length
@@ -23,12 +31,10 @@
     end
 
     @testset "test Integer conversion" begin
-        res_int = dbscan(X_int,ϵ_int, min_pts)
+        X_int = round.(Int, X.*100)
+        res_int = dbscan(X_int, ϵ*100, min_pts)
         @test eltype(res_int.df) == Float64
         @test length(res_int.labels) == size(X_int, 1)
     end
 
-    @testset "test errors" begin
-        @test_throws ArgumentError dbscan(X[:, 2], ϵ, min_pts)
-    end
 end

test/kmeans.jl

@@ -34,7 +34,7 @@
 
         @testset "diferent types of float" begin
             B = rand(Float32, 100, 4)
-            res2 = kmeans(B, k)
+            res2 = kmeans(B, k, init=:random)
             @test eltype(B) == eltype(res2.centroids[1])
         end