[SPARK-1439, SPARK-1440] Generate unified Scaladoc across projects and Javadocs

I used the sbt-unidoc plugin (https://github.com/sbt/sbt-unidoc) to create a unified Scaladoc of our public packages, and generate Javadocs as well. One limitation is that I haven't found an easy way to exclude packages in the Javadoc; there is a SBT task that identifies Java sources to run javadoc on, but it's been very difficult to modify it from outside to change what is set in the unidoc package. Some SBT-savvy people should help with this. The Javadoc site also lacks package-level descriptions and things like that, so we may want to look into that. We may decide not to post these right now if it's too limited compared to the Scala one.

Example of the built doc site: http://people.csail.mit.edu/matei/spark-unified-docs/

Author: Matei Zaharia <matei@databricks.com>

This patch had conflicts when merged, resolved by
Committer: Patrick Wendell <pwendell@gmail.com>

Closes #457 from mateiz/better-docs and squashes the following commits:

a63d4a3 [Matei Zaharia] Skip Java/Scala API docs for Python package
5ea1f43 [Matei Zaharia] Fix links to Java classes in Java guide, fix some JS for scrolling to anchors on page load
f05abc0 [Matei Zaharia] Don't include java.lang package names
995e992 [Matei Zaharia] Skip internal packages and class names with $ in JavaDoc
a14a93c [Matei Zaharia] typo
76ce64d [Matei Zaharia] Add groups to Javadoc index page, and a first package-info.java
ed6f994 [Matei Zaharia] Generate JavaDoc as well, add titles, update doc site to use unified docs
acb993d [Matei Zaharia] Add Unidoc plugin for the projects we want Unidoced

1 files changed, 5 insertions, 5 deletions

diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 4236b0c8b6..0963a99881 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -36,15 +36,15 @@ The following links provide a detailed explanation of the methods and usage exam
 # Data Types
 
 Most MLlib algorithms operate on RDDs containing vectors. In Java and Scala, the
-[Vector](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) class is used to
+[Vector](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) class is used to
 represent vectors. You can create either dense or sparse vectors using the
-[Vectors](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) factory.
+[Vectors](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) factory.
 
 In Python, MLlib can take the following vector types:
 
 * [NumPy](http://www.numpy.org) arrays
 * Standard Python lists (e.g. `[1, 2, 3]`)
-* The MLlib [SparseVector](api/pyspark/pyspark.mllib.linalg.SparseVector-class.html) class
+* The MLlib [SparseVector](api/python/pyspark.mllib.linalg.SparseVector-class.html) class
 * [SciPy sparse matrices](http://docs.scipy.org/doc/scipy/reference/sparse.html)
 
 For efficiency, we recommend using NumPy arrays over lists, and using the
@@ -52,8 +52,8 @@ For efficiency, we recommend using NumPy arrays over lists, and using the
 for SciPy matrices, or MLlib's own SparseVector class.
 
 Several other simple data types are used throughout the library, e.g. the LabeledPoint
-class ([Java/Scala](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint),
-[Python](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html)) for labeled data.
+class ([Java/Scala](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint),
+[Python](api/python/pyspark.mllib.regression.LabeledPoint-class.html)) for labeled data.
 
 # Dependencies
 MLlib uses the [jblas](https://github.com/mikiobraun/jblas) linear algebra library, which itself