From fd7eb99fb7d71e8c799f05a601f46c0001c7debd Mon Sep 17 00:00:00 2001
From: Imran Younus <iyounus@us.ibm.com>
Date: Tue, 2 Feb 2016 10:49:14 -0800
Subject: [PATCH] fixed indentation

---
 .../ml/regression/LinearRegression.scala      | 33 +++++++------------
 1 file changed, 11 insertions(+), 22 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index b82605556ed76..e253f25c0ea65 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -74,8 +74,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
   /**
    * Set the regularization parameter.
    * Default is 0.0.
-    *
-    * @group setParam
+   * @group setParam
    */
   @Since("1.3.0")
   def setRegParam(value: Double): this.type = set(regParam, value)
@@ -84,8 +83,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
   /**
    * Set if we should fit the intercept
    * Default is true.
-    *
-    * @group setParam
+   * @group setParam
    */
   @Since("1.5.0")
   def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
@@ -98,8 +96,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
    * the models should be always converged to the same solution when no regularization
    * is applied. In R's GLMNET package, the default behavior is true as well.
    * Default is true.
-    *
-    * @group setParam
+   * @group setParam
    */
   @Since("1.5.0")
   def setStandardization(value: Boolean): this.type = set(standardization, value)
@@ -110,8 +107,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
    * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
    * For 0 < alpha < 1, the penalty is a combination of L1 and L2.
    * Default is 0.0 which is an L2 penalty.
-    *
-    * @group setParam
+   * @group setParam
    */
   @Since("1.4.0")
   def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value)
@@ -120,8 +116,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
   /**
    * Set the maximum number of iterations.
    * Default is 100.
-    *
-    * @group setParam
+   * @group setParam
    */
   @Since("1.3.0")
   def setMaxIter(value: Int): this.type = set(maxIter, value)
@@ -131,8 +126,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
    * Set the convergence tolerance of iterations.
    * Smaller value will lead to higher accuracy with the cost of more iterations.
    * Default is 1E-6.
-    *
-    * @group setParam
+   * @group setParam
    */
   @Since("1.4.0")
   def setTol(value: Double): this.type = set(tol, value)
@@ -142,8 +136,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
    * Whether to over-/under-sample training instances according to the given weights in weightCol.
    * If empty, all instances are treated equally (weight 1.0).
    * Default is empty, so all instances have weight one.
-    *
-    * @group setParam
+   * @group setParam
    */
   @Since("1.6.0")
   def setWeightCol(value: String): this.type = set(weightCol, value)
@@ -157,8 +150,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
    * solution to the linear regression problem.
    * The default value is "auto" which means that the solver algorithm is
    * selected automatically.
-    *
-    * @group setParam
+   * @group setParam
    */
   @Since("1.6.0")
   def setSolver(value: String): this.type = set(solver, value)
@@ -422,8 +414,7 @@ class LinearRegressionModel private[ml] (
 
   /**
    * Evaluates the model on a testset.
-    *
-    * @param dataset Test dataset to evaluate model on.
+   * @param dataset Test dataset to evaluate model on.
    */
   // TODO: decide on a good name before exposing to public API
   private[regression] def evaluate(dataset: DataFrame): LinearRegressionSummary = {
@@ -521,8 +512,7 @@ object LinearRegressionModel extends MLReadable[LinearRegressionModel] {
  * :: Experimental ::
  * Linear regression training results. Currently, the training summary ignores the
  * training coefficients except for the objective trace.
-  *
-  * @param predictions predictions outputted by the model's `transform` method.
+ * @param predictions predictions outputted by the model's `transform` method.
  * @param objectiveHistory objective function (scaled loss + regularization) at each iteration.
  */
 @Since("1.5.0")
@@ -546,8 +536,7 @@ class LinearRegressionTrainingSummary private[regression] (
 /**
  * :: Experimental ::
  * Linear regression results evaluated on a dataset.
-  *
-  * @param predictions predictions outputted by the model's `transform` method.
+ * @param predictions predictions outputted by the model's `transform` method.
  */
 @Since("1.5.0")
 @Experimental