[SPARK-12199][DOC] Follow-up: Refine example code in ml-features.md

https://issues.apache.org/jira/browse/SPARK-12199

Follow-up PR of SPARK-11551. Fix some errors in ml-features.md

mengxr

Author: Xusen Yin <yinxusen@gmail.com>

Closes #10193 from yinxusen/SPARK-12199.

4 files changed, 15 insertions, 15 deletions

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 8b00cc652d..158f3f2018 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -63,7 +63,7 @@ the [IDF Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.IDF) for mor
 `Word2VecModel`. The model maps each word to a unique fixed-size vector. The `Word2VecModel`
 transforms each document into a vector using the average of all words in the document; this vector
 can then be used for as features for prediction, document similarity calculations, etc.
-Please refer to the [MLlib user guide on Word2Vec](mllib-feature-extraction.html#Word2Vec) for more
+Please refer to the [MLlib user guide on Word2Vec](mllib-feature-extraction.html#word2Vec) for more
 details.
 
 In the following code segment, we start with a set of documents, each of which is represented as a sequence of words. For each document, we transform it into a feature vector. This feature vector could then be passed to a learning algorithm.
@@ -411,7 +411,7 @@ for more details on the API.
 Refer to the [DCT Java docs](api/java/org/apache/spark/ml/feature/DCT.html)
 for more details on the API.
 
-{% include_example java/org/apache/spark/examples/ml/JavaDCTExample.java %}}
+{% include_example java/org/apache/spark/examples/ml/JavaDCTExample.java %}
 </div>
 </div>
 
@@ -669,7 +669,7 @@ for more details on the API.
 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">
+<div data-lang="scala" markdown="1">
 
 Refer to the [Normalizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Normalizer)
 for more details on the API.
@@ -677,7 +677,7 @@ for more details on the API.
 {% include_example scala/org/apache/spark/examples/ml/NormalizerExample.scala %}
 </div>
 
-<div data-lang="java">
+<div data-lang="java" markdown="1">
 
 Refer to the [Normalizer Java docs](api/java/org/apache/spark/ml/feature/Normalizer.html)
 for more details on the API.
@@ -685,7 +685,7 @@ for more details on the API.
 {% include_example java/org/apache/spark/examples/ml/JavaNormalizerExample.java %}
 </div>
 
-<div data-lang="python">
+<div data-lang="python" markdown="1">
 
 Refer to the [Normalizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Normalizer)
 for more details on the API.
@@ -709,7 +709,7 @@ Note that if the standard deviation of a feature is zero, it will return default
 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">
+<div data-lang="scala" markdown="1">
 
 Refer to the [StandardScaler Scala docs](api/scala/index.html#org.apache.spark.ml.feature.StandardScaler)
 for more details on the API.
@@ -717,7 +717,7 @@ for more details on the API.
 {% include_example scala/org/apache/spark/examples/ml/StandardScalerExample.scala %}
 </div>
 
-<div data-lang="java">
+<div data-lang="java" markdown="1">
 
 Refer to the [StandardScaler Java docs](api/java/org/apache/spark/ml/feature/StandardScaler.html)
 for more details on the API.
@@ -725,7 +725,7 @@ for more details on the API.
 {% include_example java/org/apache/spark/examples/ml/JavaStandardScalerExample.java %}
 </div>
 
-<div data-lang="python">
+<div data-lang="python" markdown="1">
 
 Refer to the [StandardScaler Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.StandardScaler)
 for more details on the API.
@@ -788,7 +788,7 @@ More details can be found in the API docs for [Bucketizer](api/scala/index.html#
 The following example demonstrates how to bucketize a column of `Double`s into another index-wised column.
 
 <div class="codetabs">
-<div data-lang="scala">
+<div data-lang="scala" markdown="1">
 
 Refer to the [Bucketizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Bucketizer)
 for more details on the API.
@@ -796,7 +796,7 @@ for more details on the API.
 {% include_example scala/org/apache/spark/examples/ml/BucketizerExample.scala %}
 </div>
 
-<div data-lang="java">
+<div data-lang="java" markdown="1">
 
 Refer to the [Bucketizer Java docs](api/java/org/apache/spark/ml/feature/Bucketizer.html)
 for more details on the API.
@@ -804,7 +804,7 @@ for more details on the API.
 {% include_example java/org/apache/spark/examples/ml/JavaBucketizerExample.java %}
 </div>
 
-<div data-lang="python">
+<div data-lang="python" markdown="1">
 
 Refer to the [Bucketizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Bucketizer)
 for more details on the API.
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
index 9698cac504..1eda1f694f 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
@@ -59,7 +59,7 @@ public class JavaBinarizerExample {
     DataFrame binarizedDataFrame = binarizer.transform(continuousDataFrame);
     DataFrame binarizedFeatures = binarizedDataFrame.select("binarized_feature");
     for (Row r : binarizedFeatures.collect()) {
-    Double binarized_value = r.getDouble(0);
+      Double binarized_value = r.getDouble(0);
       System.out.println(binarized_value);
     }
     // $example off$
diff --git a/examples/src/main/python/ml/polynomial_expansion_example.py b/examples/src/main/python/ml/polynomial_expansion_example.py
index 3d4fafd1a4..89f5cbe8f2 100644
--- a/examples/src/main/python/ml/polynomial_expansion_example.py
+++ b/examples/src/main/python/ml/polynomial_expansion_example.py
@@ -30,9 +30,9 @@ if __name__ == "__main__":
 
     # $example on$
     df = sqlContext\
-        .createDataFrame([(Vectors.dense([-2.0, 2.3]), ),
-                          (Vectors.dense([0.0, 0.0]), ),
-                          (Vectors.dense([0.6, -1.1]), )],
+        .createDataFrame([(Vectors.dense([-2.0, 2.3]),),
+                          (Vectors.dense([0.0, 0.0]),),
+                          (Vectors.dense([0.6, -1.1]),)],
                          ["features"])
     px = PolynomialExpansion(degree=2, inputCol="features", outputCol="polyFeatures")
     polyDF = px.transform(df)
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/ElementWiseProductExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/ElementwiseProductExample.scala
index 872de51dc7..872de51dc7 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/ElementWiseProductExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/ElementwiseProductExample.scala