Skll

- You can now include feature files that don't have class labels in your
featuresets. At least one feature file has to have a label though,
because we only support supervised learning so far.
- **Important:** If you're using TSV files in your experiments, you should
either name the class label column 'y' or use the new `tsv_label` option
in your configuration file to specify the name of the label column. This
was necessary to support feature files without labels.
- Fixed an issue with how version number was being imported in setup.py that
would prevent installation if you didn't already have the prereqs
installed on your machine.
- Made random seeds smaller to fix crash on 32-bit machines. This means that
experiments run with previous versions of skll will yield slightly
different results if you re-run them with v0.9.5+.
- Added `megam_to_csv` for converting .megam files to CSV/TSV files.
- Fixed a potential rounding problem with `csv_to_megam` that could slightly
change feature values in conversion process.
- Cleaned up test_skll.py a little bit.
- Updated documentation to include missing fields that can be specified in
config files.

- Documentation fixes
- Added requirements.txt to manifest to fix broken PyPI release

- Fixed bug with merging feature sets that used to cause a crash.
- If you're running scikit-learn 0.14+, use their StandardScaler, since the bug fix we include in FixedStandardScaler is in there.
- Unit tests all pass again
- Lots of little things related to using travis (which do not affect users)

- Fixed example.cfg path issue. Updated some documentation.
- Made path in make_example_iris_data.py consistent with the updated one in example.cfg

- Fixed bug where classification experiments would raise an error about class labels not being floats
- Updated documentation to include quick example for run_experiment.

After a couple months of cleaning things up and adding documentation, our initial public release is finally here.