wrap long lines

Author: mengxr

docs/mllib-clustering.md

@@ -39,8 +39,9 @@ a given dataset, the algorithm returns the best clustering result).
 <div data-lang="scala" markdown="1">
 Following code snippets can be executed in `spark-shell`.
 
-In the following example after loading and parsing data, we use the [`KMeans`](api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans) 
-object to cluster the data into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
+In the following example after loading and parsing data, we use the
+[`KMeans`](api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans) object to cluster the data
+into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
 Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the
 optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
 
@@ -73,10 +74,10 @@ calling `.rdd()` on your `JavaRDD` object.
 <div data-lang="python" markdown="1">
 Following examples can be tested in the PySpark shell.
 
-In the following example after loading and parsing data, we use the KMeans object to cluster the data
-into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
-Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the
-optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
+In the following example after loading and parsing data, we use the KMeans object to cluster the
+data into two clusters. The number of desired clusters is passed to the algorithm. We then compute
+Within Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In
+fact the optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
 
 {% highlight python %}
 from pyspark.mllib.clustering import KMeans
@@ -101,4 +102,4 @@ print("Within Set Sum of Squared Error = " + str(WSSSE))
 {% endhighlight %}
 </div>
 
-</div>
+</div>

docs/mllib-collaborative-filtering.md

@@ -18,39 +18,38 @@ In particular, we implement the [alternating least squares
 algorithm to learn these latent factors. The implementation in MLlib has the
 following parameters:
 
-* *numBlocks* is the number of blacks used to parallelize computation (set to -1 to auto-configure). 
+* *numBlocks* is the number of blacks used to parallelize computation (set to -1 to auto-configure).
 * *rank* is the number of latent factors in our model.
 * *iterations* is the number of iterations to run.
 * *lambda* specifies the regularization parameter in ALS.
-* *implicitPrefs* specifies whether to use the *explicit feedback* ALS variant or one adapted for *implicit feedback* data
-* *alpha* is a parameter applicable to the implicit feedback variant of ALS that governs the *baseline* confidence in preference observations
+* *implicitPrefs* specifies whether to use the *explicit feedback* ALS variant or one adapted for
+  *implicit feedback* data
+* *alpha* is a parameter applicable to the implicit feedback variant of ALS that governs the
+  *baseline* confidence in preference observations
 
 ### Explicit vs. implicit feedback
 
 The standard approach to matrix factorization based collaborative filtering treats 
 the entries in the user-item matrix as *explicit* preferences given by the user to the item.
 
-It is common in many real-world use cases to only have access to *implicit feedback* 
-(e.g. views, clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with 
-such data is taken from 
+It is common in many real-world use cases to only have access to *implicit feedback* (e.g. views,
+clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with such data is taken
+from
 [Collaborative Filtering for Implicit Feedback Datasets](http://dx.doi.org/10.1109/ICDM.2008.22).
-Essentially instead of trying to model the matrix of ratings directly, this approach treats the data as 
-a combination of binary preferences and *confidence values*. The ratings are then related 
-to the level of confidence in observed user preferences, rather than explicit ratings given to items. 
-The model then tries to find latent factors that can be used to predict the expected preference of a user
-for an item. 
+Essentially instead of trying to model the matrix of ratings directly, this approach treats the data
+as a combination of binary preferences and *confidence values*. The ratings are then related to the
+level of confidence in observed user preferences, rather than explicit ratings given to items.  The
+model then tries to find latent factors that can be used to predict the expected preference of a
+user for an item.
 
 ## Examples
 
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
-
-Following code snippets can be executed in `spark-shell`.
-
 In the following example we load rating data. Each row consists of a user, a product and a rating.
-We use the default ALS.train() method which assumes ratings are explicit. We evaluate the recommendation
-model by measuring the Mean Squared Error of rating prediction.
+We use the default ALS.train() method which assumes ratings are explicit. We evaluate the
+recommendation model by measuring the Mean Squared Error of rating prediction.
 
 {% highlight scala %}
 import org.apache.spark.mllib.recommendation.ALS
@@ -86,22 +85,16 @@ other signals), you can use the trainImplicit method to get better results.
 {% highlight scala %}
 val model = ALS.trainImplicit(ratings, 1, 20, 0.01)
 {% endhighlight %}
-
 </div>
 
 <div data-lang="java" markdown="1">
-
 All of MLlib's methods use Java-friendly types, so you can import and call them there the same
 way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
 Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
 calling `.rdd()` on your `JavaRDD` object.
-
 </div>
 
 <div data-lang="python" markdown="1">
-
-Following examples can be tested in the PySpark shell.
-
 In the following example we load rating data. Each row consists of a user, a product and a rating.
 We use the default ALS.train() method which assumes ratings are explicit. We evaluate the
 recommendation by measuring the Mean Squared Error of rating prediction.
@@ -138,4 +131,5 @@ model = ALS.trainImplicit(ratings, 1, 20)
 
 ## Tutorial
 
-[AMP Camp](http://ampcamp.berkeley.edu/) provides a hands-on tutorial for [personalized movie recommendation with MLlib](http://ampcamp.berkeley.edu/big-data-mini-course/movie-recommendation-with-mllib.html).
+[AMP Camp](http://ampcamp.berkeley.edu/) provides a hands-on tutorial for
+[personalized movie recommendation with MLlib](http://ampcamp.berkeley.edu/big-data-mini-course/movie-recommendation-with-mllib.html).