Features are wavelet transfrom approx coefficients for L1 sensor (from low-passed filtered signal) with the coiflet wavelet (I cropped the coefficients to be first 2000 since the L1 sensor series are different lengths for each subject. Not sure what the best approach for this challenge is, upon further reading dynamic time warping seems like a viable solution). Tons of overfitting.
Implemented Forward Search for feature selection
Reran 18means with forward search feature selection
-Classify.py - Fixed space/tabs intermingled
-featureGen.py - Added wavelet approx coefficient features (low-passed filtered signal), for l1 sensor cropped to 2000 because subjects have different signal lengths. Will figure out better way to cope with mismatched data but just wanted to see if wavelet transform would be useful.
-featureSelect.py - implemented forward search for feature selection
 Added wavelet approx coefficient features (low-passed filtered signal), for l1 sensor cropped to 2000 because subjects have different signal lengths. Will figure out better way to cope with mismatched data but just wanted to see if wavelet transform would be useful.
Merge branch 'master' of https://github.com/darylchang/ParkinsonsGait

Conflicts:
	classify.py
	featureGen.py
	featureGen.pyc
	results/results.txt
tested forward search for 18means in classify