use extractor for vectors in NaiveBayes.scala and Normalizer.scala

Author: coderxiang

mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala

@@ -93,10 +93,10 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with
   def run(data: RDD[LabeledPoint]) = {
     val requireNonnegativeValues: Vector => Unit = (v: Vector) => {
       val values = v match {
-        case sv: SparseVector =>
-          sv.values
-        case dv: DenseVector =>
-          dv.values
+        case SparseVector(size, indices, values) =>
+          values
+        case DenseVector(values) =>
+          values
       }
       if (!values.forall(_ >= 0.0)) {
         throw new SparkException(s"Naive Bayes requires nonnegative feature values but found $v.")

mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala

@@ -52,24 +52,24 @@ class Normalizer(p: Double) extends VectorTransformer {
       // However, for sparse vector, the `index` array will not be changed,
       // so we can re-use it to save memory.
       vector match {
-        case dv: DenseVector =>
-          val values = dv.values.clone()
+        case DenseVector(vs) =>
+          val values = vs.clone()
           val size = values.size
           var i = 0
           while (i < size) {
             values(i) /= norm
             i += 1
           }
           Vectors.dense(values)
-        case sv: SparseVector =>
-          val values = sv.values.clone()
+        case SparseVector(size, ids, vs) =>
+          val values = vs.clone()
           val nnz = values.size
           var i = 0
           while (i < nnz) {
             values(i) /= norm
             i += 1
           }
-          Vectors.sparse(sv.size, sv.indices, values)
+          Vectors.sparse(size, ids, values)
         case v => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
       }
     } else {