Bug Bash 1-10-17 for Mahout 0.13.0. Current target is a Jan 15 code freeze. ===========================================================================
Pat ---------- MAHOUT-1786: Make classes implements Serializable for Spark 1.5+ MAHOUT-1904: Create a test harness to test mahout across different hardware configurations Sebastian ---------- MAHOUT-1884: Allow specification of dimensions of a DRM Andrew ---------- MAHOUT-1791: Automatic threading for java based mmul in the front end and the backend. MAHOUT-1682: Create a documentation page for SPCA MAHOUT-1893: Fix Algorithm list on mahout.apache.org MAHOUT-1686: Create a documentattion page for ALS Dmitriy ---------- MAHOUT-1790: SparkEngine nnz overflow resultSize when reducing. Andy ---------- EPIC: MAHOUT-1862: Native Mahout integration EPIC: MAHOUT-1742 non-legacy framework related issues. MAHOUT-1860: Add Stack Image to the top of the front page of the Website MAHOUT-1879: Lazy density analysis of DRMs in CheckpointedDrm MAHOUT-1885: Inital Implementation of VCL Bindings MAHOUT-1873: Use densityAnalysis() in all necessary MAHOUT-1892: Can't broadcast vector in Mahout-Shell MAHOUT-1851: Automatic probing of in-core and back-end solvers MAHOUT-1885: Inital Implementation of VCL Bindings MAHOUT-1862: Native Mahout integration Suneel ---------- EPIC: MAHOUT-1861: New Mahout Clustering, Classification, Sketching and Optimization Algorithms MAHOUT-1870: Add import and export capabilities for DRMs to and from Apache Arrow MAHOUT-1882: SequentialAccessSparseVector inerateNonZeros is incorrect. MAHOUT-1875: Use faster shallowCopy for dense matices in blockify drm/package.blockify(..) MAHOUT-1830: Publish scaladocs for Mahout 0.12.0 release MAHOUT-1902: Parse Spark and Mahout variable arguments from the Mahout spark-shell Trevor ---------- MAHOUT-1856: Create a framework for new Mahout Clustering, Classification, and Optimization Algorithms MAHOUT-1895: Add convenience methods for converting Vectors to Scala types MAHOUT-1896: Add convenience methods for interacting with Spark ML
