fix broken test

Author: sethah

mllib/src/test/scala/org/apache/spark/ml/optim/loss/DifferentiableRegularizationSuite.scala

@@ -31,7 +31,7 @@ class DifferentiableRegularizationSuite extends SparkFunSuite {
     val regFun = new L2Regularization(regParam, shouldApply, None)
     val (loss, grad) = regFun.calculate(coefficients)
     assert(loss === 0.5 * regParam * BLAS.dot(coefficients, coefficients))
-    assert(grad === coefficients.toArray.map(_ * regParam))
+    assert(grad === Vectors.dense(coefficients.toArray.map(_ * regParam)))
 
     // check with features standard
     val featuresStd = Array(0.1, 1.1, 0.5)
@@ -40,9 +40,9 @@ class DifferentiableRegularizationSuite extends SparkFunSuite {
     val expectedLossStd = 0.5 * regParam * (0 until numFeatures).map { j =>
       coefficients(j) * coefficients(j) / (featuresStd(j) * featuresStd(j))
     }.sum
-    val expectedGradientStd = (0 until numFeatures).map { j =>
+    val expectedGradientStd = Vectors.dense((0 until numFeatures).map { j =>
       regParam * coefficients(j) / (featuresStd(j) * featuresStd(j))
-    }.toArray
+    }.toArray)
     assert(lossStd === expectedLossStd)
     assert(gradStd === expectedGradientStd)
 
@@ -51,7 +51,7 @@ class DifferentiableRegularizationSuite extends SparkFunSuite {
     val regFunApply = new L2Regularization(regParam, shouldApply2, None)
     val (lossApply, gradApply) = regFunApply.calculate(coefficients)
     assert(lossApply === 0.5 * regParam * coefficients(1) * coefficients(1))
-    assert(gradApply ===  Array(0.0, coefficients(1) * regParam, 0.0))
+    assert(gradApply ===  Vectors.dense(0.0, coefficients(1) * regParam, 0.0))
 
     // check with zero features standard
     val featuresStdZero = Array(0.1, 0.0, 0.5)