[SPARK-8140] [MLLIB] Remove empty model check in StreamingLinearAlgorithm

1. Prevent creating a map of data to find numFeatures
2. If model is empty, then initialize with a zero vector of numFeature

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #6684 from MechCoder/spark-8140 and squashes the following commits:

7fbf5f9 [MechCoder] [SPARK-8140] Remove empty model check in StreamingLinearAlgorithm And other minor cosmits

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

@@ -179,7 +179,7 @@ object GradientDescent extends Logging {
      * 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
+      weights, Vectors.zeros(weights.size), 0, 1, regParam)._2
 
     for (i <- 1 to numIterations) {
       val bcWeights = data.context.broadcast(weights)

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

@@ -195,11 +195,11 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
      */
     val initialWeights = {
       if (numOfLinearPredictor == 1) {
-        Vectors.dense(new Array[Double](numFeatures))
+        Vectors.zeros(numFeatures)
       } else if (addIntercept) {
-        Vectors.dense(new Array[Double]((numFeatures + 1) * numOfLinearPredictor))
+        Vectors.zeros((numFeatures + 1) * numOfLinearPredictor)
       } else {
-        Vectors.dense(new Array[Double](numFeatures * numOfLinearPredictor))
+        Vectors.zeros(numFeatures * numOfLinearPredictor)
       }
     }
     run(input, initialWeights)

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

@@ -87,9 +87,6 @@ abstract class StreamingLinearAlgorithm[
         model match {
           case Some(m) =>
             m.weights
-          case None =>
-            val numFeatures = rdd.first().features.size
-            Vectors.dense(numFeatures)
         }
       model = Some(algorithm.run(rdd, initialWeights))
       logInfo("Model updated at time %s".format(time.toString))

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

@@ -79,7 +79,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
     this
   }
 
-  /** Set the initial weights. Default: [0.0, 0.0]. */
+  /** Set the initial weights. */
   def setInitialWeights(initialWeights: Vector): this.type = {
     this.model = Some(algorithm.createModel(initialWeights, 0.0))
     this