[WIP] accuracy and zero one loss with jaccard index

This pr intends to bring multilabel accuracy and zero-one loss based on the jaccard index.

For reference, see section 7.1.1 of [Mining Multi-label Data](http://lkm.fri.uni-lj.si/xaigor/slo/pedagosko/dr-ui/tsoumakas09-dmkdh.pdf) and the [Wikipedia entry on Jaccard index](http://en.wikipedia.org/wiki/Jaccard_index).

TODO list:
- [ ] Check that everything works with numpy 1.3
- [x] Add multilabel accuracy based on jaccard similarity score
- [ ] write narrative doc for accuracy based on jaccard similarity score
- [ ] Add multilabel zero-one loss based on jaccard distance
- [ ] write narrative doc for multilabel zero-one loss based on jaccard distance