SPARK-2830 [MLlib]: re-organize mllib documentation

As per discussions with Xiangrui, I've reorganized and edited the mllib documentation.

Author: Ameet Talwalkar <atalwalkar@gmail.com>

Closes #1908 from atalwalkar/master and squashes the following commits:

fe6938a [Ameet Talwalkar] made xiangruis suggested changes
840028b [Ameet Talwalkar] made xiangruis suggested changes
7ec366a [Ameet Talwalkar] reorganize and edit mllib documentation

1 files changed, 95 insertions, 0 deletions

diff --git a/docs/mllib-stats.md b/docs/mllib-stats.md
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+---
+layout: global
+title: Statistics Functionality - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Statistics Functionality 
+---
+
+* Table of contents
+{:toc}
+
+
+`\[
+\newcommand{\R}{\mathbb{R}}
+\newcommand{\E}{\mathbb{E}} 
+\newcommand{\x}{\mathbf{x}}
+\newcommand{\y}{\mathbf{y}}
+\newcommand{\wv}{\mathbf{w}}
+\newcommand{\av}{\mathbf{\alpha}}
+\newcommand{\bv}{\mathbf{b}}
+\newcommand{\N}{\mathbb{N}}
+\newcommand{\id}{\mathbf{I}} 
+\newcommand{\ind}{\mathbf{1}} 
+\newcommand{\0}{\mathbf{0}} 
+\newcommand{\unit}{\mathbf{e}} 
+\newcommand{\one}{\mathbf{1}} 
+\newcommand{\zero}{\mathbf{0}}
+\]`
+
+## Data Generators 
+
+## Stratified Sampling 
+
+## Summary Statistics 
+
+### Multivariate summary statistics
+
+We provide column summary statistics for `RowMatrix` (note: this functionality is not currently supported in `IndexedRowMatrix` or `CoordinateMatrix`). 
+If the number of columns is not large, e.g., on the order of thousands, then the 
+covariance matrix can also be computed as a local matrix, which requires $\mathcal{O}(n^2)$ storage where $n$ is the
+number of columns. The total CPU time is $\mathcal{O}(m n^2)$, where $m$ is the number of rows,
+and is faster if the rows are sparse.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`computeColumnSummaryStatistics()`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) returns an instance of
+[`MultivariateStatisticalSummary`](api/scala/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary),
+which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
+total count.
+
+{% highlight scala %}
+import org.apache.spark.mllib.linalg.Matrix
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+import org.apache.spark.mllib.stat.MultivariateStatisticalSummary
+
+val mat: RowMatrix = ... // a RowMatrix
+
+// Compute column summary statistics.
+val summary: MultivariateStatisticalSummary = mat.computeColumnSummaryStatistics()
+println(summary.mean) // a dense vector containing the mean value for each column
+println(summary.variance) // column-wise variance
+println(summary.numNonzeros) // number of nonzeros in each column
+
+// Compute the covariance matrix.
+val cov: Matrix = mat.computeCovariance()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`RowMatrix#computeColumnSummaryStatistics`](api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html#computeColumnSummaryStatistics()) returns an instance of
+[`MultivariateStatisticalSummary`](api/java/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.html),
+which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
+total count.
+
+{% highlight java %}
+import org.apache.spark.mllib.linalg.Matrix;
+import org.apache.spark.mllib.linalg.distributed.RowMatrix;
+import org.apache.spark.mllib.stat.MultivariateStatisticalSummary;
+
+RowMatrix mat = ... // a RowMatrix
+
+// Compute column summary statistics.
+MultivariateStatisticalSummary summary = mat.computeColumnSummaryStatistics();
+System.out.println(summary.mean()); // a dense vector containing the mean value for each column
+System.out.println(summary.variance()); // column-wise variance
+System.out.println(summary.numNonzeros()); // number of nonzeros in each column
+
+// Compute the covariance matrix.
+Matrix cov = mat.computeCovariance();
+{% endhighlight %}
+</div>
+</div>
+
+
+## Hypothesis Testing