fold_shifted, included, hemi) elif align == 'ha_testsubj': train_resp, test_resp = get_ha_testsubj_data( test_p, mappers, fold_shifted, included, hemi) alphas = np.logspace(0, 3, 20) nboots = len(included) chunklen = 15 nchunks = 15 wt, corrs, alphas, bootstrap_corrs, valinds = hyper_ridge.bootstrap_ridge( train_stim, train_resp, test_stim, test_resp, alphas, nboots, single_alpha=True, nuisance_regressor=True) print('\nFinished training ridge regression') print('\n\nwt: {0}'.format(wt)) print('\n\n corrs: {0}'.format(corrs)) print('\n\nalphas: {0}'.format(alphas)) print('\n\nbootstrap_corrs: {0}'.format(bootstrap_corrs)) print('\n\nvalinds: {0}'.format(valinds)) print('\n\nWriting to file...') directory = os.path.join( '/dartfs/rc/lab/D/DBIC/DBIC/f0042x1/life-encoding/results/ridge-models',
load_data( os.path.join( sam_data_dir, '{0}_task-life_acq-{1}vol_run-0{2}.{3}.tproject.gii'. format(test_p, tr[3], 3, hemi))).samples[4:-7, :]) mv.zscore(test_resp, chunks_attr=None) print(test_resp.shape) run = 2 balphas = np.logspace(-3, 3, 100) nboots = 15 chunklen = 15 nchunks = 25 # print('\n\nRidge regression with alphas: {0}, nboots: {1}\n'.format(balphas, nboots)) wt, corrs, alphas, bootstrap_corrs, valinds = hyper_ridge.bootstrap_ridge( train_stim, train_resp, test_stim, test_resp, balphas, nboots, chunklen, nchunks) # rr_model = Ridge(alpha=1.0) # rr_model.fit(w2v, fmri[:,i]) print('\nFinished training ridge regression') print('\n\nwt: {0}'.format(wt)) print('\n\ncorrs: {0}'.format(corrs)) print('\n\nalphas: {0}'.format(alphas)) # print('\n\nbootstrap_corrs: {0}'.format(bootstrap_corrs)) # print('\n\nvalinds: {0}'.format(valinds)) print('\n\nWriting to file...') directory = os.path.join(ridge_dir, 'avg-{}-slh-CV'.format(stimfile[:-4]), test_p, hemi)