Github user chiwanpark commented on a diff in the pull request:
https://github.com/apache/flink/pull/861#discussion_r37138063
--- Diff: docs/libs/ml/statistics.md ---
@@ -0,0 +1,108 @@
+---
+mathjax: include
+htmlTitle: FlinkML - Statistics
+title: <a href="../ml">FlinkML</a> - Statistics
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
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+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
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+KIND, either express or implied. See the License for the
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+-->
+
+* This will be replaced by the TOC
+{:toc}
+
+## Description
+
+ The statistics utility provides features such as building histograms over
data, determining
+ mean, variance, gini impurity, entropy etc. of data.
+
+## Methods
+
+ The Statistics utility provides two major functions: `createHistogram`
and `dataStats`.
+
+### Creating a histogram
+
+ There are two types of histograms:
+ <ul>
+ <li>
+ <strong>Continuous Histograms</strong>: These histograms are formed on
a data set `X: DataSet[Double]`
+ when the values in `X` are from a continuous range. These histograms
support
+ `quantile` and `sum` operations. Here `quantile(q)` refers to a value
$x_q$ such that $|x: x
+ \leq x_q| = q * |X|$. Further, `sum(s)` refers to the number of
elements $x \leq s$, which can
+ be construed as a cumulative probability value at $s$[Of course,
<i>scaled</i> probability].
+ <br>
+ A continuous histogram can be formed by calling `X.createHistogram(b)`
where `b` is the
+ number of bins.
+ </li>
+ <li>
+ <strong>Categorical Histograms</strong>: These histograms are formed
on a data set `X:DataSet[Double]`
+ when the values in `X` are from a discrete distribution. These
histograms
+ support `count(c)` operation which returns the number of elements
associated with cateogry `c`.
+ <br>
+ A categorical histogram can be formed by calling
`X.createHistogram(0)`.
+ </li>
+ </ul>
+
+### Data Statistics
+
+ The `dataStats` function operates on a data set `X: DataSet[Vector]` and
returns column-wise
+ statistics for `X`. Every field of `X` is allowed to be defined as either
<i>discrete</i> or
+ <i>continuous</i>.
+ <br>
+ Statistics can be evaluated by calling `DataStats.dataStats(X)` or
+ `DataStats.dataStats(X, discreteFields`). The latter is used when some
fields are needed to be
+ declared discrete-valued, and is provided as an array of indices of
fields which are discrete.
+ <br>
+ The following information is available as part of `DataStats`:
+ <ul>
+ <li>Number of elements in `X`</li>
+ <li>Dimension of `X`</li>
+ <li>Column-wise statistics where for discrete fields, we report counts
for each category, and
+ the Gini impurity and Entropy of the field, while for continuous
fields, we report the
+ minimum, maximum, mean and variance.
+ </li>
+ </ul>
+
+## Examples
+
+{% highlight scala %}
+
+import org.apache.flink.ml.statistics._
+import org.apache.flink.ml._
+
+val X: DataSet[Double] = ...
+// Create continuous histogram
+val histogram = X.createHistogram(5) // creates a histogram with five
bins
+histogram.quantile(0.3) // returns the 30th quantile
+histogram.sum(4) // returns number of elements
less than 4
+
+// Create categorical histogram
+val histogram = X.createHistogram(0) // creates a categorical histogram
+histogram.count(3) // number of elements with
cateogory value 3
--- End diff --
cateogory -> category
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