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authorSean Owen <sowen@cloudera.com>2014-05-06 20:07:22 -0700
committerPatrick Wendell <pwendell@gmail.com>2014-05-06 20:07:36 -0700
commit514ee93daf20ee0778a5894042b294c437a80ed8 (patch)
treebba9a3ec197143a557f1efdba27e6a4b5d8f5b41 /docs/mllib-collaborative-filtering.md
parent8cfebf5bf2ada0ba62b9b0000c8ef9f28fa6b267 (diff)
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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 (cherry picked from commit 25ad8f93012730115a8a1fac649fe3e842c045b3) Signed-off-by: Patrick Wendell <pwendell@gmail.com>
Diffstat (limited to 'docs/mllib-collaborative-filtering.md')
-rw-r--r--docs/mllib-collaborative-filtering.md2
1 files changed, 1 insertions, 1 deletions
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index 79f5e3a7ca..f486c56e55 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -77,7 +77,7 @@ val ratesAndPreds = ratings.map{
}.join(predictions)
val MSE = ratesAndPreds.map{
case ((user, product), (r1, r2)) => math.pow((r1- r2), 2)
-}.reduce(_ + _)/ratesAndPreds.count
+}.mean()
println("Mean Squared Error = " + MSE)
{% endhighlight %}