From 58488819e8ea34cd1112c9914a4479941f9ca0ae Mon Sep 17 00:00:00 2001 From: Xusen Yin Date: Tue, 8 Dec 2015 16:07:40 +0800 Subject: [PATCH] fix errors in markdown --- docs/ml-features.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/ml-features.md b/docs/ml-features.md index 5105a948fec8e..ff3daa4fe4c52 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 %} @@ -564,7 +564,7 @@ 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.
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Refer to the [Normalizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Normalizer) for more details on the API. @@ -572,7 +572,7 @@ for more details on the API. {% include_example scala/org/apache/spark/examples/ml/NormalizerExample.scala %}
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Refer to the [Normalizer Java docs](api/java/org/apache/spark/ml/feature/Normalizer.html) for more details on the API. @@ -580,7 +580,7 @@ for more details on the API. {% include_example java/org/apache/spark/examples/ml/JavaNormalizerExample.java %}
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Refer to the [Normalizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Normalizer) for more details on the API. @@ -604,7 +604,7 @@ 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.
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Refer to the [StandardScaler Scala docs](api/scala/index.html#org.apache.spark.ml.feature.StandardScaler) for more details on the API. @@ -612,7 +612,7 @@ for more details on the API. {% include_example scala/org/apache/spark/examples/ml/StandardScalerExample.scala %}
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Refer to the [StandardScaler Java docs](api/java/org/apache/spark/ml/feature/StandardScaler.html) for more details on the API. @@ -620,7 +620,7 @@ for more details on the API. {% include_example java/org/apache/spark/examples/ml/JavaStandardScalerExample.java %}
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Refer to the [StandardScaler Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.StandardScaler) for more details on the API. @@ -683,7 +683,7 @@ 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.
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Refer to the [Bucketizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Bucketizer) for more details on the API. @@ -691,7 +691,7 @@ for more details on the API. {% include_example scala/org/apache/spark/examples/ml/BucketizerExample.scala %}
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Refer to the [Bucketizer Java docs](api/java/org/apache/spark/ml/feature/Bucketizer.html) for more details on the API. @@ -699,7 +699,7 @@ for more details on the API. {% include_example java/org/apache/spark/examples/ml/JavaBucketizerExample.java %}
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Refer to the [Bucketizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Bucketizer) for more details on the API.