[SPARK-8570] [MLLIB] [DOCS] Improve MLlib Local Matrix Documentation.

Updated MLlib Data Types Local Matrix section to include information on sparse matrices, added sparse matrix examples to the Scala and Java examples, and added Python examples for both dense and sparse matrices.

Author: Mike Dusenberry <mwdusenb@us.ibm.com>

Closes #6958 from dusenberrymw/Improve_MLlib_Local_Matrix_Documentation and squashes the following commits:

ceae407 [Mike Dusenberry] Updated MLlib Data Types Local Matrix section to include information on sparse matrices, added sparse matrix examples to the Scala and Java examples, and added Python examples for both dense and sparse matrices.

docs/mllib-data-types.md

@@ -226,7 +226,8 @@ examples = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
 
 A local matrix has integer-typed row and column indices and double-typed values, stored on a single
 machine.  MLlib supports dense matrices, whose entry values are stored in a single double array in
-column major.  For example, the following matrix `\[ \begin{pmatrix}
+column-major order, and sparse matrices, whose non-zero entry values are stored in the Compressed Sparse
+Column (CSC) format in column-major order.  For example, the following dense matrix `\[ \begin{pmatrix}
 1.0 & 2.0 \\
 3.0 & 4.0 \\
 5.0 & 6.0
@@ -238,35 +239,64 @@ is stored in a one-dimensional array `[1.0, 3.0, 5.0, 2.0, 4.0, 6.0]` with the m
 <div data-lang="scala" markdown="1">
 
 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).
+[`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide two
+implementations: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix),
+and [`SparseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.SparseMatrix).
 We recommend using the factory methods implemented
 in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$) to create local
-matrices.
+matrices. Remember, local matrices in MLlib are stored in column-major order.
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.{Matrix, Matrices}
 
 // Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
 val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
+
+// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
+val sm: Matrix = Matrices.sparse(3, 2, Array(0, 1, 3), Array(0, 2, 1), Array(9, 6, 8))
 {% endhighlight %}
 </div>
 
 <div data-lang="java" markdown="1">
 
 The base class of local matrices is
-[`Matrix`](api/java/org/apache/spark/mllib/linalg/Matrix.html), and we provide one
-implementation: [`DenseMatrix`](api/java/org/apache/spark/mllib/linalg/DenseMatrix.html).
+[`Matrix`](api/java/org/apache/spark/mllib/linalg/Matrix.html), and we provide two
+implementations: [`DenseMatrix`](api/java/org/apache/spark/mllib/linalg/DenseMatrix.html),
+and [`SparseMatrix`](api/java/org/apache/spark/mllib/linalg/SparseMatrix.html).
 We recommend using the factory methods implemented
 in [`Matrices`](api/java/org/apache/spark/mllib/linalg/Matrices.html) to create local
-matrices.
+matrices. Remember, local matrices in MLlib are stored in column-major order.
 
 {% highlight java %}
 import org.apache.spark.mllib.linalg.Matrix;
 import org.apache.spark.mllib.linalg.Matrices;
 
 // Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
 Matrix dm = Matrices.dense(3, 2, new double[] {1.0, 3.0, 5.0, 2.0, 4.0, 6.0});
+
+// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
+Matrix sm = Matrices.sparse(3, 2, new int[] {0, 1, 3}, new int[] {0, 2, 1}, new double[] {9, 6, 8});
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+The base class of local matrices is
+[`Matrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Matrix), and we provide two
+implementations: [`DenseMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.DenseMatrix),
+and [`SparseMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.SparseMatrix).
+We recommend using the factory methods implemented
+in [`Matrices`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Matrices) to create local
+matrices. Remember, local matrices in MLlib are stored in column-major order.
+
+{% highlight python %}
+import org.apache.spark.mllib.linalg.{Matrix, Matrices}
+
+// Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
+dm2 = Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])
+
+// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
+sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 2, 1], [9, 6, 8])
 {% endhighlight %}
 </div>