1 files changed, 17 insertions, 30 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java
index 9ca9a7847c..324a781c1a 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java
@@ -23,9 +23,8 @@ import org.apache.spark.api.java.JavaSparkContext;
 // $example on$
 import scala.Tuple2;
 
-import org.apache.spark.api.java.JavaDoubleRDD;
+import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.function.Function;
 import org.apache.spark.mllib.linalg.Vectors;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.mllib.regression.LinearRegressionModel;
@@ -44,43 +43,31 @@ public class JavaLinearRegressionWithSGDExample {
     // Load and parse the data
     String path = "data/mllib/ridge-data/lpsa.data";
     JavaRDD<String> data = sc.textFile(path);
-    JavaRDD<LabeledPoint> parsedData = data.map(
-      new Function<String, LabeledPoint>() {
-        public LabeledPoint call(String line) {
-          String[] parts = line.split(",");
-          String[] features = parts[1].split(" ");
-          double[] v = new double[features.length];
-          for (int i = 0; i < features.length - 1; i++) {
-            v[i] = Double.parseDouble(features[i]);
-          }
-          return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v));
-        }
+    JavaRDD<LabeledPoint> parsedData = data.map(line -> {
+      String[] parts = line.split(",");
+      String[] features = parts[1].split(" ");
+      double[] v = new double[features.length];
+      for (int i = 0; i < features.length - 1; i++) {
+        v[i] = Double.parseDouble(features[i]);
       }
-    );
+      return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v));
+    });
     parsedData.cache();
 
     // Building the model
     int numIterations = 100;
     double stepSize = 0.00000001;
-    final LinearRegressionModel model =
+    LinearRegressionModel model =
       LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), numIterations, stepSize);
 
     // Evaluate model on training examples and compute training error
-    JavaRDD<Tuple2<Double, Double>> valuesAndPreds = parsedData.map(
-      new Function<LabeledPoint, Tuple2<Double, Double>>() {
-        public Tuple2<Double, Double> call(LabeledPoint point) {
-          double prediction = model.predict(point.features());
-          return new Tuple2<>(prediction, point.label());
-        }
-      }
-    );
-    double MSE = new JavaDoubleRDD(valuesAndPreds.map(
-      new Function<Tuple2<Double, Double>, Object>() {
-        public Object call(Tuple2<Double, Double> pair) {
-          return Math.pow(pair._1() - pair._2(), 2.0);
-        }
-      }
-    ).rdd()).mean();
+    JavaPairRDD<Double, Double> valuesAndPreds = parsedData.mapToPair(point ->
+      new Tuple2<>(model.predict(point.features()), point.label()));
+
+    double MSE = valuesAndPreds.mapToDouble(pair -> {
+      double diff = pair._1() - pair._2();
+      return diff * diff;
+    }).mean();
     System.out.println("training Mean Squared Error = " + MSE);
 
     // Save and load model