Remove Java-specific constructor for Rating.

The scala constructor works for native type java types. Modify examples
to match this.

4 files changed, 6 insertions, 15 deletions

diff --git a/examples/src/main/java/spark/mllib/JavaALS.java b/examples/src/main/java/spark/mllib/JavaALS.java
index 8be079ad39..b48f459cb7 100644
--- a/examples/src/main/java/spark/mllib/JavaALS.java
+++ b/examples/src/main/java/spark/mllib/JavaALS.java
@@ -39,9 +39,9 @@ public class  JavaALS {
   static class ParseRating extends Function<String, Rating> {
     public Rating call(String line) {
       StringTokenizer tok = new StringTokenizer(line, ",");
-      Integer x = Integer.parseInt(tok.nextToken());
-      Integer y = Integer.parseInt(tok.nextToken());
-      Double rating = Double.parseDouble(tok.nextToken());
+      int x = Integer.parseInt(tok.nextToken());
+      int y = Integer.parseInt(tok.nextToken());
+      double rating = Double.parseDouble(tok.nextToken());
       return new Rating(x, y, rating);
     }
   }
diff --git a/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
index 7734c9279d..6c71dc1f32 100644
--- a/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/spark/mllib/recommendation/ALS.scala
@@ -17,9 +17,6 @@
 
 package spark.mllib.recommendation
 
-import java.lang.{Integer => JInt}
-import java.lang.{Double => JDouble}
-
 import scala.collection.mutable.{ArrayBuffer, BitSet}
 import scala.util.Random
 import scala.util.Sorting
@@ -58,13 +55,7 @@ private[recommendation] case class InLinkBlock(
 /**
  * A more compact class to represent a rating than Tuple3[Int, Int, Double].
  */
-case class Rating(val user: Int, val product: Int, val rating: Double) {
-
-  // Constructor to build a rating from java Integers and Doubles.
-  def this(user: JInt, product: JInt, rating: JDouble) = {
-    this(user.intValue(), product.intValue(), rating.doubleValue())
-  }
-}
+case class Rating(val user: Int, val product: Int, val rating: Double)
 
 /**
  * Alternating Least Squares matrix factorization.
diff --git a/mllib/src/test/scala/spark/mllib/clustering/JavaKMeansSuite.java b/mllib/src/test/scala/spark/mllib/clustering/JavaKMeansSuite.java
index a3db0c0f6d..3f2d82bfb4 100644
--- a/mllib/src/test/scala/spark/mllib/clustering/JavaKMeansSuite.java
+++ b/mllib/src/test/scala/spark/mllib/clustering/JavaKMeansSuite.java
@@ -34,7 +34,7 @@ public class JavaKMeansSuite implements Serializable {
 
   @Before
   public void setUp() {
-    sc = new JavaSparkContext("local", "JavaLogisticRegressionSuite");
+    sc = new JavaSparkContext("local", "JavaKMeans");
   }
 
   @After
diff --git a/mllib/src/test/scala/spark/mllib/recommendation/JavaALSSuite.java b/mllib/src/test/scala/spark/mllib/recommendation/JavaALSSuite.java
index 8224519792..7993629a6d 100644
--- a/mllib/src/test/scala/spark/mllib/recommendation/JavaALSSuite.java
+++ b/mllib/src/test/scala/spark/mllib/recommendation/JavaALSSuite.java
@@ -37,7 +37,7 @@ public class JavaALSSuite implements Serializable {
 
   @Before
   public void setUp() {
-    sc = new JavaSparkContext("local", "JavaLogisticRegressionSuite");
+    sc = new JavaSparkContext("local", "JavaALS");
   }
 
   @After