Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/2130#discussion_r16727951
--- Diff: docs/mllib-stats.md ---
@@ -99,69 +180,277 @@ v = u.map(lambda x: 1.0 + 2.0 * x)
</div>
-## Stratified Sampling
+## Correlations calculation
-## Summary Statistics
+Calculating the correlation between two series of data is a common
operation in Statistics. In MLlib
+we provide the flexibility to calculate pairwise correlations among many
series. The supported
+correlation methods are currently Pearson's and Spearman's correlation.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+[`Statistics`](api/scala/index.html#org.apache.spark.mllib.stat.Statistics$)
provides methods to
+calculate correlations between series. Depending on the type of input, two
`RDD[Double]`s or
+an `RDD[Vector]`, the output will be a `Double` or the correlation
`Matrix` respectively.
-### Multivariate summary statistics
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.stat.Statistics
+
+val sc: SparkContext = ...
+
+val seriesX: RDD[Double] = ... // a series
+val seriesY: RDD[Double] = ... // must have the same number of partitions
and cardinality as seriesX
+
+// compute the correlation using Pearson's method. Enter "spearman" for
Spearman's method. If a
+// method is not specified, Pearson's method will be used by default.
+val correlation: Double = Statistics.corr(seriesX, seriesY, "pearson")
+
+val data: RDD[Vector] = ... // note that each Vector is a row and not a
column
+
+// calculate the correlation matrix using Pearson's method. Use "spearman"
for Spearman's method.
+// If a method is not specified, Pearson's method will be used by default.
+val correlMatrix: Matrix = Statistics.corr(data, "pearson")
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+[`Statistics`](api/java/org/apache/spark/mllib/stat/Statistics.html)
provides methods to
+calculate correlations between series. Depending on the type of input, two
`JavaDoubleRDD`s or
+a `JavaRDD<Vector>`, the output will be a `Double` or the correlation
`Matrix` respectively.
+
+{% highlight java %}
+import org.apache.spark.api.java.JavaDoubleRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.*;
+import org.apache.spark.mllib.stat.Statistics;
+
+JavaSparkContext jsc = ...
+
+JavaDoubleRDD seriesX = ... // a series
+JavaDoubleRDD seriesY = ... // must have the same number of partitions and
cardinality as seriesX
+
+// compute the correlation using Pearson's method. Enter "spearman" for
Spearman's method. If a
+// method is not specified, Pearson's method will be used by default.
+Double correlation = Statistics.corr(seriesX.srdd(), seriesY.srdd(),
"pearson");
+
+JavaRDD<Vector> data = ... // note that each Vector is a row and not a
column
+
+// calculate the correlation matrix using Pearson's method. Use "spearman"
for Spearman's method.
+// If a method is not specified, Pearson's method will be used by default.
+Matrix correlMatrix = Statistics.corr(data.rdd(), "pearson");
+
+{% endhighlight %}
+</div>
-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 data-lang="python" markdown="1">
+[`Statistics`](api/python/pyspark.mllib.stat.Statistics-class.html)
provides methods to
+calculate correlations between series. Depending on the type of input, two
`RDD[Double]`s or
+an `RDD[Vector]`, the output will be a `Double` or the correlation
`Matrix` respectively.
+
+{% highlight python %}
+from pyspark.mllib.stat import Statistics
+
+sc = ... # SparkContext
+
+seriesX = ... # a series
+seriesY = ... # must have the same number of partitions and cardinality as
seriesX
+
+# Compute the correlation using Pearson's method. Enter "spearman" for
Spearman's method. If a
+# method is not specified, Pearson's method will be used by default.
+print Statistics.corr(seriesX, seriesY, method="pearson")
+
+data = ... # an RDD of Vectors
+# calculate the correlation matrix using Pearson's method. Use "spearman"
for Spearman's method.
+# If a method is not specified, Pearson's method will be used by default.
+print Statistics.corr(data, method="pearson")
+
+{% endhighlight %}
+</div>
+
+</div>
+
+## Stratified sampling
+
+Unlike the other statistics functions, which reside in MLLib, stratified
sampling methods,
+`sampleByKey` and `sampleByKeyExact`, can be performed on RDD's of
key-value pairs. For stratified
+sampling, the keys can be thought of as a label and the value as a
specific attribute. For example
+the key can be man or woman, or document ids, and the respective values
can be the list of ages
+of the people in the population or the list of words in the documents. A
separate method for exact
--- End diff --
Maybe we should mention `sampleByKey` first and let users know this doesn't
give the exact sample size.
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