Several feature vectors for A-Domain substrate data

A-Domain substrate data added. A-domain AA sequence used to create feature vectors. Substrates are the different classes.

The different feature vectors created:
1) Using 5 groupings of AA's (accounting for characteristics such as acidity and polarity), map each AA to a point in a vector corresponding to a group.
2) Using 5 groupings of AA's, count how many of each type of AA occured.
3) Count occurences of each of 20 AA's.
4) Count n-gram occurences (n=1,2,3).

Preliminary results:
Splitting the data into 1100 training and 450 test,
- % correct on test data ranged from ~60 to ~75
- linear kernel slightly outperforms poly, which outperforms rbf
- 2-grams significantly better than 1-grams. 3-grams slightly better than 2-grams
- n-grams outperformed 1), which slightly outperformed 2)
- several combinations of features did not improve accuracy, but sample size ttoo small to be conclusive
* fewer training points improves performance: linear kernel using 2-grams with 900 training points produced 80% accuracy