[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

4 files changed, 38 insertions, 38 deletions

diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index a765b1c4f7..cd43982191 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -79,15 +79,15 @@ class LogisticRegressionWithSGD(object):
         """
         Train a logistic 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:
@@ -151,15 +151,15 @@ class SVMWithSGD(object):
         """
         Train a support vector machine 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 regParam:          The regularizer parameter (default: 1.0).
-        @param miniBatchFraction: Fraction of data to be used for each SGD
+        :param regParam:          The regularizer parameter (default: 1.0).
+        :param miniBatchFraction: Fraction of data to be used for each SGD
                                   iteration.
-        @param initialWeights:    The initial weights (default: None).
-        @param regType:           The type of regularizer used for training
+        :param initialWeights:    The initial weights (default: None).
+        :param regType:           The type of regularizer used for training
                                   our model.
 
                                   :Allowed values:
@@ -238,10 +238,10 @@ class NaiveBayes(object):
         classification.  By making every vector a 0-1 vector, it can also be
         used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).
 
-        @param data: RDD of NumPy vectors, one per element, where the first
+        :param data: RDD of NumPy vectors, one per element, where the first
                coordinate is the label and the rest is the feature vector
                (e.g. a count vector).
-        @param lambda_: The smoothing parameter
+        :param lambda_: The smoothing parameter
         """
         sc = data.context
         jlist = sc._jvm.PythonMLLibAPI().trainNaiveBayes(data._to_java_object_rdd(), lambda_)
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 51014a8ceb..24c5480b2f 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -238,8 +238,8 @@ class SparseVector(Vector):
         (index, value) pairs, or two separate arrays of indices and
         values (sorted by index).
 
-        @param size: Size of the vector.
-        @param args: Non-zero entries, as a dictionary, list of tupes,
+        :param size: Size of the vector.
+        :param args: Non-zero entries, as a dictionary, list of tupes,
                or two sorted lists containing indices and values.
 
         >>> print SparseVector(4, {1: 1.0, 3: 5.5})
@@ -458,8 +458,8 @@ class Vectors(object):
         (index, value) pairs, or two separate arrays of indices and
         values (sorted by index).
 
-        @param size: Size of the vector.
-        @param args: Non-zero entries, as a dictionary, list of tupes,
+        :param size: Size of the vector.
+        :param args: Non-zero entries, as a dictionary, list of tupes,
                      or two sorted lists containing indices and values.
 
         >>> print Vectors.sparse(4, {1: 1.0, 3: 5.5})
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:
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 8233d4e81f..1357fd4fbc 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -77,10 +77,10 @@ class MLUtils(object):
         method parses each line into a LabeledPoint, where the feature
         indices are converted to zero-based.
 
-        @param sc: Spark context
-        @param path: file or directory path in any Hadoop-supported file
+        :param sc: Spark context
+        :param path: file or directory path in any Hadoop-supported file
                      system URI
-        @param numFeatures: number of features, which will be determined
+        :param numFeatures: number of features, which will be determined
                             from the input data if a nonpositive value
                             is given. This is useful when the dataset is
                             already split into multiple files and you
@@ -88,7 +88,7 @@ class MLUtils(object):
                             features may not present in certain files,
                             which leads to inconsistent feature
                             dimensions.
-        @param minPartitions: min number of partitions
+        :param minPartitions: min number of partitions
         @return: labeled data stored as an RDD of LabeledPoint
 
         >>> from tempfile import NamedTemporaryFile
@@ -126,8 +126,8 @@ class MLUtils(object):
         """
         Save labeled data in LIBSVM format.
 
-        @param data: an RDD of LabeledPoint to be saved
-        @param dir: directory to save the data
+        :param data: an RDD of LabeledPoint to be saved
+        :param dir: directory to save the data
 
         >>> from tempfile import NamedTemporaryFile
         >>> from fileinput import input
@@ -149,10 +149,10 @@ class MLUtils(object):
         """
         Load labeled points saved using RDD.saveAsTextFile.
 
-        @param sc: Spark context
-        @param path: file or directory path in any Hadoop-supported file
+        :param sc: Spark context
+        :param path: file or directory path in any Hadoop-supported file
                      system URI
-        @param minPartitions: min number of partitions
+        :param minPartitions: min number of partitions
         @return: labeled data stored as an RDD of LabeledPoint
 
         >>> from tempfile import NamedTemporaryFile