don't use CENTROIDS in test

Author: EmilHvitfeldt

tests/testthat/test-k_means_diagnostics.R

@@ -1,17 +1,19 @@
 test_that("kmeans sse metrics work", {
+  set.seed(1234)
   kmeans_fit_stats <- k_means(num_clusters = mtcars[1:3, ]) %>%
     set_engine("stats", algorithm = "MacQueen") %>%
     fit(~., mtcars)
 
+  # We don't use CENTROIDS argument becuase it breaks testing for different
+  # versions of ClusterR #186
   kmeans_fit_ClusterR <- k_means(num_clusters = 3) %>%
-    set_engine("ClusterR", CENTROIDS = as.matrix(mtcars[1:3, ])) %>%
+    set_engine("ClusterR") %>%
     fit(~., mtcars)
 
   km_orig <- kmeans(mtcars, centers = mtcars[1:3, ], algorithm = "MacQueen")
   km_orig_2 <- ClusterR::KMeans_rcpp(
     data = mtcars,
-    clusters = 3,
-    CENTROIDS = as.matrix(mtcars[1:3, ])
+    clusters = 3
   )
 
   expect_equal(sse_within(kmeans_fit_stats)$wss,
@@ -92,7 +94,7 @@ test_that("kmeans sihouette metrics work", {
     fit(~., mtcars)
 
   kmeans_fit_ClusterR <- k_means(num_clusters = 3) %>%
-    set_engine("ClusterR", CENTROIDS = as.matrix(mtcars[1:3, ])) %>%
+    set_engine("ClusterR") %>%
     fit(~., mtcars)
 
   new_data <- mtcars[1:4, ]
@@ -124,7 +126,7 @@ test_that("kmeans sihouette metrics work with new data", {
     fit(~., mtcars)
 
   kmeans_fit_ClusterR <- k_means(num_clusters = 3) %>%
-    set_engine("ClusterR", CENTROIDS = as.matrix(mtcars[1:3, ])) %>%
+    set_engine("ClusterR") %>%
     fit(~., mtcars)
 
   new_data <- mtcars[1:4, ]