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| author | Timothy Hunter <timhunter@databricks.com> | 2016-04-29 23:13:03 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2016-04-29 23:13:03 -0700 |
| commit | bc36fe6e896ab0e64f6334b1e3fd6386d0c38238 (patch) | |
| tree | 76f351715be2485233b45f0e676b533fbce9ed7b /launcher/src/main/java/org/apache | |
| parent | 43b149fb885a27f9467aab28e5195f6f03aadcf0 (diff) | |
| download | spark-bc36fe6e896ab0e64f6334b1e3fd6386d0c38238.tar.gz spark-bc36fe6e896ab0e64f6334b1e3fd6386d0c38238.tar.bz2 spark-bc36fe6e896ab0e64f6334b1e3fd6386d0c38238.zip | |
[SPARK-14831][SPARKR] Make the SparkR MLlib API more consistent with Spark
## What changes were proposed in this pull request?
This PR splits the MLlib algorithms into two flavors:
- the R flavor, which tries to mimic the existing R API for these algorithms (and works as an S4 specialization for Spark dataframes)
- the Spark flavor, which follows the same API and naming conventions as the rest of the MLlib algorithms in the other languages
In practice, the former calls the latter.
## How was this patch tested?
The tests for the various algorithms were adapted to be run against both interfaces.
Author: Timothy Hunter <timhunter@databricks.com>
Closes #12789 from thunterdb/14831.
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