tests(kmeans): adapt results to last changes.

tests/test_kmeans.py

@@ -94,27 +94,26 @@ def test_KMeansMachine_var_and_weight():
         np.testing.assert_equal(weights, weights_result)
 
 
-def test_kmeans_fit():
-    np.random.seed(0)
-    data1 = np.random.normal(loc=1, size=(2000, 3))
-    data2 = np.random.normal(loc=-1, size=(2000, 3))
-    print(data1.min(), data1.max())
-    print(data2.min(), data2.max())
-    data = np.concatenate([data1, data2], axis=0)
+def test_kmeans_fit_parallel():
+    rs_gen = np.random.RandomState(0)
+    data1 = rs_gen.normal(loc=1, size=(2000, 3))
+    data2 = rs_gen.normal(loc=-1, size=(2000, 3))
+    data = np.vstack([data1, data2])
 
     for transform in (to_numpy, to_dask_array):
         data = transform(data)
         machine = KMeansMachine(2, random_state=0).fit(data)
         centroids = machine.centroids_[np.argsort(machine.centroids_[:, 0])]
         expected = [
-            [-1.07173464, -1.06200356, -1.00724920],
+            [-1.07173464, -1.06200356, -1.0072492],
             [0.99479125, 0.99665564, 0.97689017],
         ]
         np.testing.assert_almost_equal(centroids, expected, decimal=7)
 
         # Early stop
-        machine = KMeansMachine(2, max_iter=2)
+        machine = KMeansMachine(2, max_iter=2, random_state=0)
         machine.fit(data)
+        np.testing.assert_almost_equal(centroids, expected, decimal=7)
 
 
 def test_kmeans_fit_init_pp():