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https://issues.apache.org/jira/browse/FLINK-1718?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14384070#comment-14384070
]
ASF GitHub Bot commented on FLINK-1718:
---------------------------------------
Github user rmetzger commented on a diff in the pull request:
https://github.com/apache/flink/pull/539#discussion_r27307815
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/math/DenseVector.scala
---
@@ -67,7 +63,25 @@ case class DenseVector(val values: Array[Double])
extends Vector {
* @return Copy of the vector instance
*/
override def copy: Vector = {
- DenseVector(values.clone())
+ DenseVector(data.clone())
+ }
+
+ /** Updates the element at the given index with the provided value
+ *
+ * @param index
+ * @param value
+ */
+ override def update(index: Int, value: Double): Unit = {
+ require(0 <= index && index < data.length, s"Index $index is out of
bounds " +
--- End diff --
Might be inefficient.
> Add sparse vector and sparse matrix types to machine learning library
> ---------------------------------------------------------------------
>
> Key: FLINK-1718
> URL: https://issues.apache.org/jira/browse/FLINK-1718
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Till Rohrmann
> Labels: ML
>
> Currently, the machine learning library only supports dense matrix and dense
> vectors. For future algorithms it would be beneficial to also support sparse
> vectors and matrices.
> I'd propose to use the compressed sparse column (CSC) representation, because
> it allows rather efficient operations compared to a map backed sparse
> matrix/vector implementation. Furthermore, this is also the format the Breeze
> library expects for sparse matrices/vectors. Thus, it is easy to convert to a
> sparse breeze data structure which provides us with many linear algebra
> operations.
> BIDMat [1] uses the same data representation.
> Resources:
> [1] [https://github.com/BIDData/BIDMat]
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