From 5e068e3c37d146c9cde0801d321b69709a87b591 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 4 Feb 2015 22:41:37 -0800
Subject: [PATCH] [MLlib] Minor: UDF style update.

---
 .../apache/spark/ml/classification/LogisticRegression.scala   | 4 +++-
 .../main/scala/org/apache/spark/ml/recommendation/ALS.scala   | 4 ++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index df90078de148f..bdb498e565b88 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -137,7 +137,9 @@ class LogisticRegressionModel private[ml] (
       1.0 / (1.0 + math.exp(-margin))
     } : Double)
     val t = map(threshold)
-    val predictFunction = udf((score: Double) => { if (score > t) 1.0 else 0.0 } : Double)
+    val predictFunction = udf { score: Double =>
+      if (score > t) 1.0 else 0.0
+    }
     dataset
       .select($"*", scoreFunction(col(map(featuresCol))).as(map(scoreCol)))
       .select($"*", predictFunction(col(map(scoreCol))).as(map(predictionCol)))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index c7bec7a845a11..aa6d89030727b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -129,13 +129,13 @@ class ALSModel private[ml] (
 
     // Register a UDF for DataFrame, and then
     // create a new column named map(predictionCol) by running the predict UDF.
-    val predict = udf((userFeatures: Seq[Float], itemFeatures: Seq[Float]) => {
+    val predict = udf { (userFeatures: Seq[Float], itemFeatures: Seq[Float]) =>
       if (userFeatures != null && itemFeatures != null) {
         blas.sdot(k, userFeatures.toArray, 1, itemFeatures.toArray, 1)
       } else {
         Float.NaN
       }
-    } : Float)
+    }
     dataset
       .join(users, dataset(map(userCol)) === users("id"), "left")
       .join(items, dataset(map(itemCol)) === items("id"), "left")