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author | Kousuke Saruta <sarutak@oss.nttdata.co.jp> | 2014-10-23 09:19:32 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2014-10-23 09:19:32 -0700 |
commit | f799700eec4a5e33db9b2d6a4bee60a50fd5a099 (patch) | |
tree | 2fdb00c74e6dc8ef637f37e9f094e7547d3c0b31 | |
parent | d6a302539213e8cdb51ca14b1769aeb33f9f435f (diff) | |
download | spark-f799700eec4a5e33db9b2d6a4bee60a50fd5a099.tar.gz spark-f799700eec4a5e33db9b2d6a4bee60a50fd5a099.tar.bz2 spark-f799700eec4a5e33db9b2d6a4bee60a50fd5a099.zip |
[SPARK-4055][MLlib] Inconsistent spelling 'MLlib' and 'MLLib'
Thare are some inconsistent spellings 'MLlib' and 'MLLib' in some documents and source codes.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes #2903 from sarutak/SPARK-4055 and squashes the following commits:
b031640 [Kousuke Saruta] Fixed inconsistent spelling "MLlib and MLLib"
5 files changed, 5 insertions, 5 deletions
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md index 1511ae6dda..1162241449 100644 --- a/docs/mllib-feature-extraction.md +++ b/docs/mllib-feature-extraction.md @@ -83,7 +83,7 @@ val idf = new IDF().fit(tf) val tfidf: RDD[Vector] = idf.transform(tf) {% endhighlight %} -MLLib's IDF implementation provides an option for ignoring terms which occur in less than a +MLlib's IDF implementation provides an option for ignoring terms which occur in less than a minimum number of documents. In such cases, the IDF for these terms is set to 0. This feature can be used by passing the `minDocFreq` value to the IDF constructor. diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md index c463241399..10a5131c07 100644 --- a/docs/mllib-statistics.md +++ b/docs/mllib-statistics.md @@ -197,7 +197,7 @@ print Statistics.corr(data, method="pearson") ## Stratified sampling -Unlike the other statistics functions, which reside in MLLib, stratified sampling methods, +Unlike the other statistics functions, which reside in MLlib, stratified sampling methods, `sampleByKey` and `sampleByKeyExact`, can be performed on RDD's of key-value pairs. For stratified sampling, the keys can be thought of as a label and the value as a specific attribute. For example the key can be man or woman, or document ids, and the respective values can be the list of ages diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java index 8d381d4e0a..95a430f1da 100644 --- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java @@ -32,7 +32,7 @@ import java.util.regex.Pattern; import scala.Tuple2; /** - * Example using MLLib ALS from Java. + * Example using MLlib ALS from Java. */ public final class JavaALS { diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java index f796123a25..e575eedeb4 100644 --- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java @@ -30,7 +30,7 @@ import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; /** - * Example using MLLib KMeans from Java. + * Example using MLlib KMeans from Java. */ public final class JavaKMeans { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala index 87bdc8558a..c67a6d3ae6 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala @@ -18,7 +18,7 @@ package org.apache.spark.mllib.api /** - * Internal support for MLLib Python API. + * Internal support for MLlib Python API. * * @see [[org.apache.spark.mllib.api.python.PythonMLLibAPI]] */ |