Merge pull request #679 from ryanlecompte/master

Make binSearch method tail-recursive for RidgeRegression

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
+import scala.collection.mutable
+
 /**
  * Ridge Regression from Joseph Gonzalez's implementation in MLBase
  */
@@ -14,7 +16,7 @@ class RidgeRegressionModel(
     val weights: DoubleMatrix,
     val intercept: Double,
     val lambdaOpt: Double,
-    val lambdas: List[(Double, Double, DoubleMatrix)])
+    val lambdas: Seq[(Double, Double, DoubleMatrix)])
   extends RegressionModel {
 
   override def predict(testData: RDD[Array[Double]]): RDD[Double] = {
@@ -98,21 +100,29 @@ 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)
+    def binSearch(low: Double, high: Double): Seq[(Double, Double, DoubleMatrix)] = {
+      val buffer = mutable.ListBuffer.empty[(Double, Double, DoubleMatrix)]
+
+      @tailrec
+      def loop(low: Double, high: Double): Seq[(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) {
+          buffer += lowValue += highValue
+          loop(newLow, newHigh)
+        } else {
+          buffer += lowValue += highValue
+          buffer.result()
+        }
       }
+
+      loop(low, high)
     }
 
     // Actually compute the best lambda
@@ -134,6 +144,7 @@ class RidgeRegression private (var lambdaLow: Double, var lambdaHigh: Double)
     model
   }
 }
+
 /**
  * Top-level methods for calling Ridge Regression.
  */