Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6127#discussion_r30862751
--- Diff: docs/ml-features.md ---
@@ -618,5 +634,157 @@ indexedData = indexerModel.transform(data)
</div>
</div>
+
+## Normalizer
+
+`Normalizer` is a `Transformer` which transforms a dataset of `Vector`
rows, normalizing each `Vector` to have unit norm. It takes parameter `p`,
which specifies the
[p-norm](http://en.wikipedia.org/wiki/Norm_%28mathematics%29#p-norm) used for
normalization. ($p = 2$ by default.) This normalization can help standardize
your input data and improve the behavior of learning algorithms.
+
+The following example demonstrates how to load a dataset in libsvm format
and then normalize each row to have unit $L^2$ norm and unit $L^\infty$ norm.
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.ml.feature.Normalizer
+import org.apache.spark.mllib.util.MLUtils
+
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+val dataFrame = sqlContext.createDataFrame(data)
+val normalizer = new
Normalizer().setInputCol("features").setOutputCol("normFeatures")
+
+// Normalize each Vector using $L^2$ norm.
+val l2NormData = normalizer.transform(dataFrame, normalizer.p -> 2)
+
+// Normalize each Vector using $L^\infty$ norm.
+val lInfNormData = normalizer.transform(dataFrame, normalizer.p ->
Double.PositiveInfinity)
+{% endhighlight %}
+</div>
+
+<div data-lang="java">
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.Normalizer;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.sql.DataFrame;
+
+JavaRDD<LabeledPoint> data =
+ MLUtils.loadLibSVMFile(jsc.sc(),
"data/mllib/sample_libsvm_data.txt").toJavaRDD();
+DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+Normalizer normalizer = new Normalizer()
+ .setInputCol("features")
+ .setOutputCol("normFeatures");
+
+// Normalize each Vector using $L^2$ norm.
+DataFrame l2NormData = normalizer.transform(dataFrame,
normalizer.p().w(2));
+
+// Normalize each Vector using $L^\infty$ norm.
+DataFrame lInfNormData =
+ normalizer.transform(dataFrame,
normalizer.p().w(Double.POSITIVE_INFINITY));
+{% endhighlight %}
+</div>
+
+<div data-lang="python">
+{% highlight python %}
+from pyspark.mllib.util import MLUtils
+from pyspark.ml.feature import Normalizer
+
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+dataFrame = sqlContext.createDataFrame(data)
+normalizer = Normalizer(inputCol="features", outputCol="normFeatures")
+
+# Normalize each Vector using $L^2$ norm.
+l2NormData = normalizer.transform(dataFrame, {normalizer.p:2.0})
+
+# Normalize each Vector using $L^\infty$ norm.
+lInfNormData = normalizer.transform(dataFrame, {normalizer.p:float("inf")})
+{% endhighlight %}
+</div>
+</div>
+
+
+## StandardScaler
+
+`StandardScaler` transforms a dataset of `Vector` rows, normalizing each
feature to have unit standard deviation and/or zero mean. It takes parameters:
+
+* `withStd`: True by default. Scales the data to unit standard deviation.
+* `withMean`: False by default. Centers the data with mean before scaling.
It will build a dense output, so this does not work on sparse input and will
raise an exception.
+
+`StandardScaler` is a `Model` which can be `fit` on a dataset to produce a
`StandardScalerModel`; this amounts to computing summary statistics. The model
can then transform a `Vector` column in a dataset to have unit standard
deviation and/or zero mean features.
+
+Note that if the standard deviation of a feature is zero, it will return
default `0.0` value in the `Vector` for that feature.
+
+More details can be found in the API docs for
+[StandardScaler](api/scala/index.html#org.apache.spark.ml.feature.StandardScaler)
and
+[StandardScalerModel](api/scala/index.html#org.apache.spark.ml.feature.StandardScalerModel).
+
+The following example demonstrates how to load a dataset in libsvm format
and then normalize each feature to have unit standard deviation.
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.ml.feature.StandardScaler
+import org.apache.spark.mllib.util.MLUtils
+
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+val dataFrame = sqlContext.createDataFrame(data)
+val scaler = new StandardScaler()
+ .setInputCol("features")
+ .setOutputCol("normFeatures")
--- End diff --
minor: `normFeatures` -> `scaledFeatures` or `stdFeatures`?
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