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authorXusen Yin <yinxusen@gmail.com>2015-12-12 17:47:01 -0800
committerJoseph K. Bradley <joseph@databricks.com>2015-12-12 17:47:01 -0800
commit98b212d36b34ab490c391ea2adf5b141e4fb9289 (patch)
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parent03138b67d3ef7f5278ea9f8b9c75f0e357ef79d8 (diff)
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[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.
Diffstat (limited to 'docs')
-rw-r--r--docs/ml-features.md22
1 files changed, 11 insertions, 11 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.