You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@mahout.apache.org by Suneel Marthi <sm...@apache.org> on 2016/05/19 00:23:33 UTC
[ANNOUNCE] Apache Mahout 0.12.1 Release
The Apache Mahout PMC is pleased to announce the release of Mahout 0.12.1
which is a minor release following 0.12.0 release on April 11, 2016.
Mahout's goal is to create an environment for quickly creating machine
learning applications that scale and run on the highest performance
parallel computation engines available. Mahout comprises an interactive
environment and library that supports generalized scalable linear algebra
and includes many modern machine learning algorithms.
Mahout 0.12.1 is a maintenance release over Mahout 0.12.0 addresses the
following issues with Apache Flink integration:
MAHOUT-1859: Disable non working msurf and mgrid before Mahout 0.12.1
release
MAHOUT-1848: drmSampleKRows in FlinkEngine should generate a dense or
sparse matrix
MAHOUT-1847: drmSampleRows in FlinkEngine doesn't wrap Int Keys when
ClassTag is of type Int
MAHOUT-1841: Matrices.symmetricUniformView(...) returning values in the
wrong range.
MAHOUT-1836:Order and add missing paramters for
DictionaryVectorizer.createTermFrequencyVectors() javadoc parameter
comments.
MAHOUT-1835 Remove countsPerPartition in Flink/blas/package.scala
MAHOUT-1834: Setup Travis CI for Mahout
MAHOUT-1833: Enhance svec function to accept cardinality as parameter
MAHOUT-1832: Upgrade Jackson version and references to 2.x
MAHOUT-1827: Suggested changes to homepage, how to contribute
Upgrade to Apache Flink 1.0.3
Experimental Mahout 2d and 3d plotting
Many thanks to all Apache committers and contributors. Special thanks to Shane
Curcuru
<https://issues.apache.org/jira/secure/ViewProfile.jspa?name=curcuru>,
Edmond Luo and <mutekinoootoko at gmail dot com> for their contributions.
Future Roadmap:
1.
Zeppelin integration for Mahout on Spark.
2.
Plotting Capabilities for Mahout matrices and DRMs
3.
Many Online and Batch Algorithm additions.
4.
Support for Native Optimizations.
5.
Performance enhancements for Samsara Framework.
6.
Performance enhancements for Algebraic Operations.