make binSearch a tail-recursive method

mllib/src/main/scala/spark/mllib/regression/RidgeRegression.scala

 package spark.mllib.regression
 
 import spark.{Logging, RDD, SparkContext}
-import spark.SparkContext._
 import spark.mllib.util.MLUtils
 
 import org.jblas.DoubleMatrix
 import org.jblas.Solve
 
+import scala.annotation.tailrec
+
 /**
  * Ridge Regression from Joseph Gonzalez's implementation in MLBase
  */
@@ -99,20 +100,28 @@ class RidgeRegression private (var lambdaLow: Double, var lambdaHigh: Double)
 
     // Binary search for the best assignment to lambda.
     def binSearch(low: Double, high: Double): List[(Double, Double, DoubleMatrix)] = {
-      val mid = (high - low) / 2 + low
-      val lowValue = crossValidate((mid - low) / 2 + low)
-      val highValue = crossValidate((high - mid) / 2 + mid)
-      val (newLow, newHigh) = if (lowValue._2 < highValue._2) {
-        (low, mid + (high-low)/4)
-      } else {
-        (mid - (high-low)/4, high)
-      }
-      if (newHigh - newLow > 1.0E-7) {
-        // :: is list prepend in Scala.
-        lowValue :: highValue :: binSearch(newLow, newHigh)
-      } else {
-        List(lowValue, highValue)
+      @tailrec
+      def loop(
+        low: Double,
+        high: Double,
+        acc: List[(Double, Double, DoubleMatrix)]): List[(Double, Double, DoubleMatrix)] = {
+        val mid = (high - low) / 2 + low
+        val lowValue = crossValidate((mid - low) / 2 + low)
+        val highValue = crossValidate((high - mid) / 2 + mid)
+        val (newLow, newHigh) = if (lowValue._2 < highValue._2) {
+          (low, mid + (high-low)/4)
+        } else {
+          (mid - (high-low)/4, high)
+        }
+        if (newHigh - newLow > 1.0E-7) {
+          // :: is list prepend in Scala.
+          loop(newLow, newHigh, lowValue :: highValue :: acc)
+        } else {
+          lowValue :: highValue :: acc
+        }
       }
+
+      loop(low, high, Nil)
     }
 
     // Actually compute the best lambda