fix test

mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala

@@ -135,18 +135,14 @@ class GaussianMixtureModel private[ml] (
   @Since("2.2.0")
   def computeLogLikelihood(dataset: Dataset[_]): Double = {
     SchemaUtils.checkColumnType(dataset.schema, $(featuresCol), new VectorUDT)
-
     val spark = dataset.sparkSession
     import spark.implicits._
 
-    val bcWeightAndDists =
-      spark.sparkContext.broadcast(weights.zip(gaussians))
-
+    val bcWeightAndDists = spark.sparkContext.broadcast(weights.zip(gaussians))
     dataset.select(col($(featuresCol))).map {
-      case Row(point: Vector) =>
+      case Row(feature: Vector) =>
         val likelihood = bcWeightAndDists.value.map {
-          case (weight, dist) =>
-            EPSILON + weight * dist.pdf(point)
+          case (weight, dist) => EPSILON + weight * dist.pdf(feature)
         }.sum
         math.log(likelihood)
     }.reduce(_ + _)

mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala

@@ -140,16 +140,6 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext
 
     model.setSummary(None)
     assert(!model.hasSummary)
-
-    // Check validity of LogLikelihood
-    transformed.select(probabilityColName).rdd.map(_.getAs[Vector](0))
-    val llk = transformed.select(probabilityColName).rdd.map(_.getAs[Vector](0))
-      .map { probs =>
-        val likelihood = probs.toArray.zip(model.weights).map {
-          case (a, b) => a * b }.sum
-        math.log(likelihood)
-    }.sum()
-    assert(model.computeLogLikelihood(dataset) ~== llk relTol 1E-8)
   }
 
   test("check LogLikelihood") {