style(machinelearning): remove unused import

Author: Tejashribambal19

src/test/java/com/thealgorithms/machinelearning/KMeansTest.java

@@ -2,7 +2,6 @@
 
 import static org.junit.jupiter.api.Assertions.assertArrayEquals;
 import static org.junit.jupiter.api.Assertions.assertThrows;
-
 import org.junit.jupiter.api.Test;
 
 class KMeansTest {
@@ -123,7 +122,7 @@ void testEmptyClusterHandling() {
 
         int[] result = KMeans.cluster(points, centroids, 100, 0.0001);
 
-        assertArrayEquals(new int[] {0, 0, 0}, result);
+        assertArrayEquals(new int[]{0, 0, 0}, result);
     }
 
     @Test
@@ -136,4 +135,54 @@ void testImmediateConvergence() {
 
         assertArrayEquals(expected, KMeans.cluster(points, centroids, 100, 0.000001));
     }
+
+    @Test
+    void testFirstPointNull() {
+        double[][] points = {
+            null
+        };
+
+        double[][] centroids = {
+            {1.0, 1.0}
+        };
+
+        assertThrows(
+                IllegalArgumentException.class,
+                () -> KMeans.cluster(points, centroids, 100, 0.0001)
+        );
+    }
+
+    @Test
+    void testNullPointInDataset() {
+        double[][] points = {
+            {1.0, 1.0},
+            null
+        };
+
+        double[][] centroids = {
+            {1.0, 1.0}
+        };
+
+        assertThrows(
+                IllegalArgumentException.class,
+                () -> KMeans.cluster(points, centroids, 100, 0.0001)
+        );
+    }
+
+    @Test
+    void testNullCentroidRow() {
+        double[][] points = {
+            {1.0, 1.0}
+        };
+
+        double[][] centroids = {
+            null
+        };
+
+        assertThrows(
+                IllegalArgumentException.class,
+                () -> KMeans.cluster(points, centroids, 100, 0.0001)
+        );
+    }
+
 }