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, 6 deletions

diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index ebb555f974..40b7a7f807 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -63,7 +63,7 @@ methods MLlib supports:
   <tbody>
     <tr>
       <td>hinge loss</td><td>$\max \{0, 1-y \wv^T \x \}, \quad y \in \{-1, +1\}$</td>
-      <td>$\begin{cases}-y \cdot \x & \text{if $y \wv^T \x <1$}, \\ 0 &
+      <td>$\begin{cases}-y \cdot \x &amp; \text{if $y \wv^T \x &lt;1$}, \\ 0 &amp;
 \text{otherwise}.\end{cases}$</td>
     </tr>
     <tr>
@@ -225,10 +225,11 @@ algorithm for 200 iterations.
 import org.apache.spark.mllib.optimization.L1Updater
 
 val svmAlg = new SVMWithSGD()
-svmAlg.optimizer.setNumIterations(200)
-  .setRegParam(0.1)
-  .setUpdater(new L1Updater)
-val modelL1 = svmAlg.run(parsedData)
+svmAlg.optimizer.
+  setNumIterations(200).
+  setRegParam(0.1).
+  setUpdater(new L1Updater)
+val modelL1 = svmAlg.run(training)
 {% endhighlight %}
 
 Similarly, you can use replace `SVMWithSGD` by
@@ -322,7 +323,7 @@ val valuesAndPreds = parsedData.map { point =>
   val prediction = model.predict(point.features)
   (point.label, prediction)
 }
-val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.reduce(_ + _) / valuesAndPreds.count
+val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean()
 println("training Mean Squared Error = " + MSE)
 {% endhighlight %}