SPARK-1727. Correct small compile errors, typos, and markdown issues in (primarly) MLlib docs

While play-testing the Scala and Java code examples in the MLlib docs, I noticed a number of small compile errors, and some typos. This led to finding and fixing a few similar items in other docs.

Then in the course of building the site docs to check the result, I found a few small suggestions for the build instructions. I also found a few more formatting and markdown issues uncovered when I accidentally used maruku instead of kramdown.

Author: Sean Owen <sowen@cloudera.com>

Closes #653 from srowen/SPARK-1727 and squashes the following commits:

6e7c38a [Sean Owen] Final doc updates - one more compile error, and use of mean instead of sum and count
8f5e847 [Sean Owen] Fix markdown syntax issues that maruku flags, even though we use kramdown (but only those that do not affect kramdown's output)
99966a9 [Sean Owen] Update issue tracker URL in docs
23c9ac3 [Sean Owen] Add Scala Naive Bayes example, to use existing example data file (whose format needed a tweak)
8c81982 [Sean Owen] Fix small compile errors and typos across MLlib docs

1 files changed, 7 insertions, 0 deletions

diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md
index 4e9ecf7c00..ab24663cfe 100644
--- a/docs/mllib-dimensionality-reduction.md
+++ b/docs/mllib-dimensionality-reduction.md
@@ -44,6 +44,10 @@ say, less than $1000$, but many rows, which we call *tall-and-skinny*.
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
+import org.apache.spark.mllib.linalg.Matrix
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+import org.apache.spark.mllib.linalg.SingularValueDecomposition
+
 val mat: RowMatrix = ...
 
 // Compute the top 20 singular values and corresponding singular vectors.
@@ -74,6 +78,9 @@ and use them to project the vectors into a low-dimensional space.
 The number of columns should be small, e.g, less than 1000.
 
 {% highlight scala %}
+import org.apache.spark.mllib.linalg.Matrix
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+
 val mat: RowMatrix = ...
 
 // Compute the top 10 principal components.