[SPARK-1260]: faster construction of features with intercept

The current implementation uses `Array(1.0, features: _*)` to construct a new array with intercept. This is not efficient for big arrays because `Array.apply` uses a for loop that iterates over the arguments. `Array.+:` is a better choice here.

Also, I don't see a reason to set initial weights to ones. So I set them to zeros.

JIRA: https://spark-project.atlassian.net/browse/SPARK-1260

Author: Xiangrui Meng <meng@databricks.com>

Closes #161 from mengxr/sgd and squashes the following commits:

b5cfc53 [Xiangrui Meng] set default weights to zeros
a1439c2 [Xiangrui Meng] faster construction of features with intercept

mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala

@@ -119,7 +119,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    */
   def run(input: RDD[LabeledPoint]) : M = {
     val nfeatures: Int = input.first().features.length
-    val initialWeights = Array.fill(nfeatures)(1.0)
+    val initialWeights = new Array[Double](nfeatures)
     run(input, initialWeights)
   }
 
@@ -134,15 +134,15 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
       throw new SparkException("Input validation failed.")
     }
 
-    // Add a extra variable consisting of all 1.0's for the intercept.
+    // Prepend an extra variable consisting of all 1.0's for the intercept.
     val data = if (addIntercept) {
-      input.map(labeledPoint => (labeledPoint.label, Array(1.0, labeledPoint.features:_*)))
+      input.map(labeledPoint => (labeledPoint.label, labeledPoint.features.+:(1.0)))
     } else {
       input.map(labeledPoint => (labeledPoint.label, labeledPoint.features))
     }
 
     val initialWeightsWithIntercept = if (addIntercept) {
-      Array(1.0, initialWeights:_*)
+      initialWeights.+:(1.0)
     } else {
       initialWeights
     }