if args.align_algo != 'noalign' :
workspace = np.load(options['working_path']+args.align_algo+'__'+str(new_niter)+'.npz')
workspace2 = np.load(options['working_path']+args.align_algo+'__'+str(new_niter)+'.npz')
# load transformation matrices into transform_lrh for projecting testing data
if args.loo == None:
(transform, tmp) = form_transformation_matrix.transform(args,
workspace, workspace2, nsubjs)
else:
(transform, tmp) = form_transformation_matrix_loo.transform(args,
workspace, workspace2,
align_data, copy.deepcopy(align_data), nsubjs)
workspace.close()
workspace2.close()
else:
new_niter = 10
(transform, tmp) = form_transformation_matrix_noalign.transform(args, nsubjs)
# transformed mkdg data with learned transformation matrices
transformed_data = np.zeros((args.nfeature , nTR_pred ,nsubjs))
for m in range(nsubjs):
trfed_tmp = transform[:,:,m].T.dot(pred_data[:,:,m])
transformed_data[:,:,m] = stats.zscore( trfed_tmp.T ,axis=0, ddof=1).T
#transformed_data[:,:,m] = trfed_tmp
# experiment
if args.exptype == 'imgpred':
if args.loo == None:
accu = expt.predict(transformed_data, args, trn_label, tst_label)
else:
# load transformation matrices
if args.align_algo != 'noalign' :
workspace_lh = np.load(options['working_path']+args.align_algo+'_lh_'+str(new_niter_lh)+'.npz')
workspace_rh = np.load(options['working_path']+args.align_algo+'_rh_'+str(new_niter_rh)+'.npz')
# load transformation matrices into transform_lrh for projecting testing data
if args.loo == None:
(transform_lh, transform_rh) = form_transformation_matrix.transform(args,
workspace_lh, workspace_rh, nsubjs)
else:
(transform_lh, transform_rh) = form_transformation_matrix_loo.transform(args,
workspace_lh, workspace_rh,
align_data_lh, align_data_rh, nsubjs)
else:
new_niter_lh = new_niter_rh = 10
(transform_lh, transform_rh)=form_transformation_matrix_noalign.transform(args,nsubjs)
# transformed mkdg data with learned transformation matrices
transformed_data = np.zeros((args.nfeature*2 , nTR_pred ,nsubjs))
for m in range(nsubjs):
trfed_lh_tmp = transform_lh[:,:,m].T.dot(pred_data_lh[:,:,m])
trfed_rh_tmp = transform_rh[:,:,m].T.dot(pred_data_rh[:,:,m])
transformed_data[:,:,m] = stats.zscore( np.vstack((trfed_lh_tmp,trfed_rh_tmp)).T ,axis=0, ddof=1).T
# experiment
if args.exptype == 'imgpred':
if args.loo == None:
accu = expt.predict(transformed_data, args, trn_label, tst_label)
else:
accu = expt.predict_loo(transformed_data, args, trn_label, tst_label)
