| commit | 55fd981c684d3d8744d9189d06da28e97b25a6ac | [log] [tgz] |
|---|---|---|
| author | EmergentOrder <lecaran@gmail.com> | Sun Oct 25 10:33:46 2015 -0300 |
| committer | EmergentOrder <lecaran@gmail.com> | Sun Oct 25 10:33:46 2015 -0300 |
| tree | 4622f8d4bde3f7aeb3a84a5416476cf0b8c1763b | |
| parent | 36fed5b9ef060eb99394bc0102798345d0e22cf3 [diff] | |
| parent | d9d3e49de0efe52ca540d1b82e0ff03d59f91cd2 [diff] |
Merge pull request #3 from EmergentOrder/master Add support for BIDMach & VW LR + SPPMI feature vectorization
Look at the following tutorial for a Quick Start guide and implementation details.
Modified PreparedData to use MLLib hashing and tf-idf implementations.
Fixed dot product implementation in the predict methods to work with batch predict method for evaluation.
Included three different data sets: e-mail spam, 20 newsgroups, and the rotten tomatoes semantic analysis set. Includes Multinomial Logistic Regression algorithm for text classification.
Fixed import script bug occuring with Python 2.
Changed data import Python script to pull straight from the 20 newsgroups page.