[SPARK-3412] [PySpark] Replace Epydoc with Sphinx to generate Python API docs

Retire Epydoc, use Sphinx to generate API docs.

Refine Sphinx docs, also convert some docstrings into Sphinx style.

It looks like:
![api doc](https://cloud.githubusercontent.com/assets/40902/4538272/9e2d4f10-4dec-11e4-8d96-6e45a8fe51f9.png)

Author: Davies Liu <davies.liu@gmail.com>

Closes #2689 from davies/docs and squashes the following commits:

bf4a0a5 [Davies Liu] fix links
3fb1572 [Davies Liu] fix _static in jekyll
65a287e [Davies Liu] fix scripts and logo
8524042 [Davies Liu] Merge branch 'master' of github.com:apache/spark into docs
d5b874a [Davies Liu] Merge branch 'master' of github.com:apache/spark into docs
4bc1c3c [Davies Liu] refactor
746d0b6 [Davies Liu] @param -> :param
240b393 [Davies Liu] replace epydoc with sphinx doc

1 files changed, 9 insertions, 9 deletions

diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 54f34a9833..12b322aaae 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -31,8 +31,8 @@ class LabeledPoint(object):
     """
     The features and labels of a data point.
 
-    @param label: Label for this data point.
-    @param features: Vector of features for this point (NumPy array, list,
+    :param label: Label for this data point.
+    :param features: Vector of features for this point (NumPy array, list,
         pyspark.mllib.linalg.SparseVector, or scipy.sparse column matrix)
     """
 
@@ -145,15 +145,15 @@ class LinearRegressionWithSGD(object):
         """
         Train a linear regression model on the given data.
 
-        @param data:              The training data.
-        @param iterations:        The number of iterations (default: 100).
-        @param step:              The step parameter used in SGD
+        :param data:              The training data.
+        :param iterations:        The number of iterations (default: 100).
+        :param step:              The step parameter used in SGD
                                   (default: 1.0).
-        @param miniBatchFraction: Fraction of data to be used for each SGD
+        :param miniBatchFraction: Fraction of data to be used for each SGD
                                   iteration.
-        @param initialWeights:    The initial weights (default: None).
-        @param regParam:          The regularizer parameter (default: 1.0).
-        @param regType:           The type of regularizer used for training
+        :param initialWeights:    The initial weights (default: None).
+        :param regParam:          The regularizer parameter (default: 1.0).
+        :param regType:           The type of regularizer used for training
                                   our model.
 
                                   :Allowed values: