[MLLIB] org.apache.spark.mllib.util.SVMDataGenerator generates ArrayIndexOutOfBoundsException. I have found the bug and tested the solution.

## What changes were proposed in this pull request?

Just adjust the size of an array in line 58 so it does not cause an ArrayOutOfBoundsException in line 66.

## How was this patch tested?

Manual tests. I have recompiled the entire project with the fix, it has been built successfully and I have run the code, also with good results.

line 66: val yD = blas.ddot(trueWeights.length, x, 1, trueWeights, 1) + rnd.nextGaussian() * 0.1
crashes because trueWeights has length "nfeatures + 1" while "x" has length "features", and they should have the same length.

To fix this just make trueWeights be the same length as x.

I have recompiled the project with the change and it is working now:
[spark-1.6.1]$ spark-submit --master local[*] --class org.apache.spark.mllib.util.SVMDataGenerator mllib/target/spark-mllib_2.11-1.6.1.jar local /home/user/test

And it generates the data successfully now in the specified folder.

Author: José Antonio <[email protected]>

Closes apache#13895 from j4munoz/patch-2.

(cherry picked from commit a3c7b41)
Signed-off-by: Sean Owen <[email protected]>
(cherry picked from commit 24d59fb)

Author: j4munoz

mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala

@@ -55,7 +55,7 @@ object SVMDataGenerator {
     val sc = new SparkContext(sparkMaster, "SVMGenerator")
 
     val globalRnd = new Random(94720)
-    val trueWeights = Array.fill[Double](nfeatures + 1)(globalRnd.nextGaussian())
+    val trueWeights = Array.fill[Double](nfeatures)(globalRnd.nextGaussian())
 
     val data: RDD[LabeledPoint] = sc.parallelize(0 until nexamples, parts).map { idx =>
       val rnd = new Random(42 + idx)