Most of the data loading and pre-treatment done
Going the wrong way. Should be using beta maps. Will correct
Correlation Matrix and rudimentary svm in cross_val
commit before merge
Exported function for showing stimuli to another example file
changed pl to plt everywhere
Removed comments in quotes. Replaced by frame of # signs
Removed @@@@@ Michael comments in source in favour of discussion in diff and cleaner code
Scrapped the whole GLM + similarity matrix stuff in favour of two decoding schemes, one in the given ventral temporal ROI, one on full brain. Both perform way above chance, but the ROI one is better
why is the default kernel of that svm package the rbf???
changed prints and comments
pylab -> matplotlib.pylab
updated svm scores in comment - yes, linear does better
PEP8
removed unnecessary lines
Removed some comments, set n_jobs to 1, print mean score + stdev, changed pylab to pyplot
turned haxby stimulus presentation back into script
removed resting state blocks. Makes scores better on full brain, worse in ROI. scores are marked in code
Scrapped detrending for the ROI, scores up to same level as full brain ...... is this scary?
taking a look at the other ROIs in the dataset