[SPARK-7671] Fix wrong URLs in MLlib Data Types Documentation

There is a mistake in the URL of Matrices in the MLlib Data Types documentation (Local matrix scala section), the URL points to https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Matrices which is a mistake, since Matrices is an object that implements factory methods for Matrix that does not have a companion class. The correct link should point to https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$

There is another mistake, in the Local Vector section in Scala, Java and Python

In the Scala section the URL of Vectors points to the trait Vector (https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Vector) and not to the factory methods implemented in Vectors.

The correct link should be: https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$

In the Java section the URL of Vectors points to the Interface Vector (https://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/linalg/Vector.html) and not to the Class Vectors

The correct link should be:
https://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/linalg/Vectors.html

In the Python section the URL of Vectors points to the class Vector (https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vector) and not the Class Vectors

The correct link should be:
https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vectors

Author: FavioVazquez <favio.vazquezp@gmail.com>

Closes apache#6196 from FavioVazquez/fix-typo-matrices-mllib-datatypes and squashes the following commits:

3e9efd5 [FavioVazquez] - Fixed wrong URLs in the MLlib Data Types Documentation
9af7074 [FavioVazquez] Merge remote-tracking branch 'upstream/master'
edab1ef [FavioVazquez] Merge remote-tracking branch 'upstream/master'
b2e2f8c [FavioVazquez] Merge remote-tracking branch 'upstream/master'

docs/mllib-data-types.md

@@ -31,7 +31,7 @@ The base class of local vectors is
 implementations: [`DenseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseVector) and
 [`SparseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.SparseVector).  We recommend
 using the factory methods implemented in
-[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors.
+[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) to create local vectors.
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -57,7 +57,7 @@ The base class of local vectors is
 implementations: [`DenseVector`](api/java/org/apache/spark/mllib/linalg/DenseVector.html) and
 [`SparseVector`](api/java/org/apache/spark/mllib/linalg/SparseVector.html).  We recommend
 using the factory methods implemented in
-[`Vectors`](api/java/org/apache/spark/mllib/linalg/Vector.html) to create local vectors.
+[`Vectors`](api/java/org/apache/spark/mllib/linalg/Vectors.html) to create local vectors.
 
 {% highlight java %}
 import org.apache.spark.mllib.linalg.Vector;
@@ -84,7 +84,7 @@ and the following as sparse vectors:
   with a single column
 
 We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented
-in [`Vectors`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vector) to create sparse vectors.
+in [`Vectors`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vectors) to create sparse vectors.
 
 {% highlight python %}
 import numpy as np
@@ -241,7 +241,7 @@ The base class of local matrices is
 [`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one
 implementation: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix).
 We recommend using the factory methods implemented
-in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices) to create local
+in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$) to create local
 matrices.
 
 {% highlight scala %}