From 4134ff0c657efcbf0f61eff0423215afd6132837 Mon Sep 17 00:00:00 2001
From: "wm624@hotmail.com" <wm624@hotmail.com>
Date: Tue, 17 May 2016 15:20:47 +0200
Subject: [SPARK-14434][ML] User guide doc and examples for GaussianMixture in
 spark.ml

## What changes were proposed in this pull request?

(Please fill in changes proposed in this fix)

Add guide doc and examples for GaussianMixture in Spark.ml in Java, Scala and Python.

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)

Manual compile and test all examples

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes #12788 from wangmiao1981/example.
---
 docs/ml-clustering.md | 82 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 82 insertions(+)

(limited to 'docs')

diff --git a/docs/ml-clustering.md b/docs/ml-clustering.md
index a0955a3855..33e4b7b0d2 100644
--- a/docs/ml-clustering.md
+++ b/docs/ml-clustering.md
@@ -148,3 +148,85 @@ Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.
 {% include_example python/ml/bisecting_k_means_example.py %}
 </div>
 </div>
+
+## Gaussian Mixture Model (GMM)
+
+A [Gaussian Mixture Model](http://en.wikipedia.org/wiki/Mixture_model#Multivariate_Gaussian_mixture_model)
+represents a composite distribution whereby points are drawn from one of *k* Gaussian sub-distributions,
+each with its own probability. The `spark.ml` implementation uses the
+[expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
+algorithm to induce the maximum-likelihood model given a set of samples.
+
+`GaussianMixture` is implemented as an `Estimator` and generates a `GaussianMixtureModel` as the base
+model.
+
+### Input Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>featuresCol</td>
+      <td>Vector</td>
+      <td>"features"</td>
+      <td>Feature vector</td>
+    </tr>
+  </tbody>
+</table>
+
+### Output Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>predictionCol</td>
+      <td>Int</td>
+      <td>"prediction"</td>
+      <td>Predicted cluster center</td>
+    </tr>
+    <tr>
+      <td>probabilityCol</td>
+      <td>Vector</td>
+      <td>"probability"</td>
+      <td>Probability of each cluster</td>
+    </tr>
+  </tbody>
+</table>
+
+### Example
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.GaussianMixture) for more details.
+
+{% include_example scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala %}
+</div>
+
+<div data-lang="java" markdown="1">
+Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/GaussianMixture.html) for more details.
+
+{% include_example java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java %}
+</div>
+
+<div data-lang="python" markdown="1">
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.GaussianMixture) for more details.
+
+{% include_example python/ml/gaussian_mixture_example.py %}
+</div>
+</div>
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