diff options
Diffstat (limited to 'mllib')
| -rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala index c4b433499a..8a16284118 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala @@ -81,11 +81,11 @@ class GradientDescentSuite extends FunSuite with LocalSparkContext with ShouldMa // Add a extra variable consisting of all 1.0's for the intercept. val testData = GradientDescentSuite.generateGDInput(A, B, nPoints, 42) val data = testData.map { case LabeledPoint(label, features) => - label -> Vectors.dense(1.0, features.toArray: _*) + label -> Vectors.dense(1.0 +: features.toArray) } val dataRDD = sc.parallelize(data, 2).cache() - val initialWeightsWithIntercept = Vectors.dense(1.0, initialWeights: _*) + val initialWeightsWithIntercept = Vectors.dense(1.0 +: initialWeights.toArray) val (_, loss) = GradientDescent.runMiniBatchSGD( dataRDD, @@ -111,7 +111,7 @@ class GradientDescentSuite extends FunSuite with LocalSparkContext with ShouldMa // Add a extra variable consisting of all 1.0's for the intercept. val testData = GradientDescentSuite.generateGDInput(2.0, -1.5, 10000, 42) val data = testData.map { case LabeledPoint(label, features) => - label -> Vectors.dense(1.0, features.toArray: _*) + label -> Vectors.dense(1.0 +: features.toArray) } val dataRDD = sc.parallelize(data, 2).cache() |