clusters name

Class_Mod/KMEANS_.py

@@ -19,13 +19,6 @@ class Sk_Kmeans:
     def fit_optimal(self, nclusters):
         model = KMeans(n_clusters = nclusters, init = 'k-means++', random_state = 42)
         model.fit(self.x)
-        yp = model.predict(self.x)
-        num_colors = nclusters
-        colors = ['#' + ''.join([random.choice('0123456789ABCDEF') for _ in range(6)]) for _ in range(num_colors)]
-        col = np.array(['#' + ''.join([random.choice('0123456789ABCDEF') for _ in range(6)]) for _ in range(self.x.shape[0])])
-        for i in range(nclusters):
-            ss = np.where(yp==i)
-            col[ss] = colors[i]
-
-
-        return self.x, col
\ No newline at end of file
+        yp = model.predict(self.x)+1
+        clu = [f'cluster#{i}' for i in yp]
+        return self.x, clu
\ No newline at end of file