[SPARK-7671] Fix wrong URLs in MLlib Data Types Documentation
There is a mistake in the URL of Matrices in the MLlib Data Types documentation (Local matrix scala section), the URL points to https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Matrices which is a mistake, since Matrices is an object that implements factory methods for Matrix that does not have a companion class. The correct link should point to https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$ There is another mistake, in the Local Vector section in Scala, Java and Python In the Scala section the URL of Vectors points to the trait Vector (https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Vector) and not to the factory methods implemented in Vectors. The correct link should be: https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$ In the Java section the URL of Vectors points to the Interface Vector (https://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/linalg/Vector.html) and not to the Class Vectors The correct link should be: https://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/linalg/Vectors.html In the Python section the URL of Vectors points to the class Vector (https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vector) and not the Class Vectors The correct link should be: https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vectors Author: FavioVazquez <favio.vazquezp@gmail.com> Closes #6196 from FavioVazquez/fix-typo-matrices-mllib-datatypes and squashes the following commits: 3e9efd5 [FavioVazquez] - Fixed wrong URLs in the MLlib Data Types Documentation 9af7074 [FavioVazquez] Merge remote-tracking branch 'upstream/master' edab1ef [FavioVazquez] Merge remote-tracking branch 'upstream/master' b2e2f8c [FavioVazquez] Merge remote-tracking branch 'upstream/master'
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@ -31,7 +31,7 @@ The base class of local vectors is
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implementations: [`DenseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseVector) and
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[`SparseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.SparseVector). We recommend
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using the factory methods implemented in
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[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors.
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[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) to create local vectors.
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{% highlight scala %}
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import org.apache.spark.mllib.linalg.{Vector, Vectors}
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@ -57,7 +57,7 @@ The base class of local vectors is
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implementations: [`DenseVector`](api/java/org/apache/spark/mllib/linalg/DenseVector.html) and
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[`SparseVector`](api/java/org/apache/spark/mllib/linalg/SparseVector.html). We recommend
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using the factory methods implemented in
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[`Vectors`](api/java/org/apache/spark/mllib/linalg/Vector.html) to create local vectors.
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[`Vectors`](api/java/org/apache/spark/mllib/linalg/Vectors.html) to create local vectors.
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{% highlight java %}
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import org.apache.spark.mllib.linalg.Vector;
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@ -84,7 +84,7 @@ and the following as sparse vectors:
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with a single column
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We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented
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in [`Vectors`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vector) to create sparse vectors.
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in [`Vectors`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vectors) to create sparse vectors.
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{% highlight python %}
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import numpy as np
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@ -241,7 +241,7 @@ The base class of local matrices is
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[`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one
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implementation: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix).
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We recommend using the factory methods implemented
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in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices) to create local
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in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$) to create local
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matrices.
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{% highlight scala %}
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