# should i zscore?
lidx = np.arange(32)
pidx = np.arange(32, 64)
lres = cv(betas[lidx].copy())
lresults.append(lres)
print "language: " + str(np.mean(lres.samples))
cv.untrain()
pres = cv(betas[pidx].copy())
presults.append(pres)
print "pictures: " + str(np.mean(pres.samples))
cv.untrain()
fclf.train(betas[lidx].copy())
l2presults.append(np.mean(fclf.predict(betas[pidx]) == betas[pidx].sa.targets))
fclf.untrain()
fclf.train(betas[pidx])
p2lresults.append(np.mean(fclf.predict(betas[lidx]) == betas[lidx].sa.targets))
import matplotlib.pyplot as plt
def plotsubs(lr, pr, l2p, p2l, c=None, title=None, bar_width=.2, opacity=.4, error_config={'ecolor': '0.3'}):
# results is the concatenated output of cv across subjects... or something.
f, (ax1, ax2) = plt.subplots(2, figsize=(12,6))
index = np.arange(len(lr))
lheights = []
lerrbars = []
pheights = []
perrbars = []
for i in lr:
