[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, 4 insertions, 4 deletions

diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
index 396b98d52a..c79cc3d944 100644
--- a/docs/mllib-optimization.md
+++ b/docs/mllib-optimization.md
@@ -95,12 +95,12 @@ As an alternative to just use the subgradient `$R'(\wv)$` of the regularizer in
 direction, an improved update for some cases can be obtained by using the proximal operator
 instead.
 For the L1-regularizer, the proximal operator is given by soft thresholding, as implemented in
-[L1Updater](api/mllib/index.html#org.apache.spark.mllib.optimization.L1Updater).
+[L1Updater](api/scala/index.html#org.apache.spark.mllib.optimization.L1Updater).
 
 
 ## Update Schemes for Distributed SGD
 The SGD implementation in
-[GradientDescent](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent) uses
+[GradientDescent](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent) uses
 a simple (distributed) sampling of the data examples.
 We recall that the loss part of the optimization problem `$\eqref{eq:regPrimal}$` is
 `$\frac1n \sum_{i=1}^n L(\wv;\x_i,y_i)$`, and therefore `$\frac1n \sum_{i=1}^n L'_{\wv,i}$` would
@@ -138,7 +138,7 @@ are developed, see the
 section for example.
 
 The SGD method
-[GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent)
+[GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent)
 has the following parameters:
 
 * `gradient` is a class that computes the stochastic gradient of the function
@@ -161,6 +161,6 @@ each iteration, to compute the gradient direction.
 
 Available algorithms for gradient descent:
 
-* [GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent)
+* [GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent)