Used return to avoid indentation

mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala

@@ -166,52 +166,51 @@ object GradientDescent extends Logging {
     if (numExamples == 0) {
 
       logInfo("GradientDescent.runMiniBatchSGD returning initial weights, no data found")
-      (initialWeights, stochasticLossHistory.toArray)
+      return (initialWeights, stochasticLossHistory.toArray)
 
-    } else {
+    }
 
-      // Initialize weights as a column vector
-      var weights = Vectors.dense(initialWeights.toArray)
-      val n = weights.size
+    // Initialize weights as a column vector
+    var weights = Vectors.dense(initialWeights.toArray)
+    val n = weights.size
+
+    /**
+     * For the first iteration, the regVal will be initialized as sum of weight squares
+     * if it's L2 updater; for L1 updater, the same logic is followed.
+     */
+    var regVal = updater.compute(
+      weights, Vectors.dense(new Array[Double](weights.size)), 0, 1, regParam)._2
+
+    for (i <- 1 to numIterations) {
+      // Sample a subset (fraction miniBatchFraction) of the total data
+      // compute and sum up the subgradients on this subset (this is one map-reduce)
+      val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42 + i)
         .treeAggregate((BDV.zeros[Double](weights.size), 0.0))(
+          seqOp = (c, v) => (c, v) match {
+            case ((grad, loss), (label, features)) =>
+              val l = gradient.compute(features, label, weights, Vectors.fromBreeze(grad))
+              (grad, loss + l)
+          },
+          combOp = (c1, c2) => (c1, c2) match {
+            case ((grad1, loss1), (grad2, loss2)) =>
+              (grad1 += grad2, loss1 + loss2)
+          })
 
       /**
-       * For the first iteration, the regVal will be initialized as sum of weight squares
-       * if it's L2 updater; for L1 updater, the same logic is followed.
+       * NOTE(Xinghao): lossSum is computed using the weights from the previous iteration
+       * and regVal is the regularization value computed in the previous iteration as well.
        */
-      var regVal = updater.compute(
-        weights, Vectors.dense(new Array[Double](weights.size)), 0, 1, regParam)._2
-
-      for (i <- 1 to numIterations) {
-        // Sample a subset (fraction miniBatchFraction) of the total data
-        // compute and sum up the subgradients on this subset (this is one map-reduce)
-        val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42 + i)
-          .aggregate((BDV.zeros[Double](weights.size), 0.0))(
-            seqOp = (c, v) => (c, v) match {
-              case ((grad, loss), (label, features)) =>
-                val l = gradient.compute(features, label, weights, Vectors.fromBreeze(grad))
-                (grad, loss + l)
-            },
-            combOp = (c1, c2) => (c1, c2) match {
-              case ((grad1, loss1), (grad2, loss2)) =>
-                (grad1 += grad2, loss1 + loss2)
-            })
-
-        /**
-         * NOTE(Xinghao): lossSum is computed using the weights from the previous iteration
-         * and regVal is the regularization value computed in the previous iteration as well.
-         */
-        stochasticLossHistory.append(lossSum / miniBatchSize + regVal)
-        val update = updater.compute(
-          weights, Vectors.fromBreeze(gradientSum / miniBatchSize), stepSize, i, regParam)
-        weights = update._1
-        regVal = update._2
-      }
-
-      logInfo("GradientDescent.runMiniBatchSGD finished. Last 10 stochastic losses %s".format(
-        stochasticLossHistory.takeRight(10).mkString(", ")))
-
-      (weights, stochasticLossHistory.toArray)
+      stochasticLossHistory.append(lossSum / miniBatchSize + regVal)
+      val update = updater.compute(
+        weights, Vectors.fromBreeze(gradientSum / miniBatchSize), stepSize, i, regParam)
+      weights = update._1
+      regVal = update._2
     }
+
+    logInfo("GradientDescent.runMiniBatchSGD finished. Last 10 stochastic losses %s".format(
+      stochasticLossHistory.takeRight(10).mkString(", ")))
+
+    (weights, stochasticLossHistory.toArray)
+
   }
 }