def obj_func(obj_ws_lf, obj_ws_rf):
(transform_lh, transform_rh) = form_transformation_matrix_loo.transform(args_tmp, obj_ws_lf, obj_ws_rf,
align_data_lh, align_data_rh,
args_tmp.nsubjs)
obj_val_tmp = 0
for m in range(args_tmp.nsubjs):
if args_tmp.align_algo in ['ha','ha_sm_retraction']:
obj_val_tmp += np.linalg.norm(transform_lh[:, :, m].T.dot(align_data_lh[:, :, m]) - obj_ws_lf['G'].T, 'fro')
obj_val_tmp += np.linalg.norm(transform_rh[:, :, m].T.dot(align_data_rh[:, :, m]) - obj_ws_rf['G'].T, 'fro')
elif args_tmp.align_algo in ['ha_syn','pha_em','ha_syn_noagg']:
obj_val_tmp += np.linalg.norm(align_data_lh[:, :, m] - transform_lh[:, :, m].dot(obj_ws_lf['G'].T), 'fro')
obj_val_tmp += np.linalg.norm(align_data_rh[:, :, m] - transform_rh[:, :, m].dot(obj_ws_rf['G'].T), 'fro')
return obj_val_tmp
# make sure right and left brain alignment are working at the same iterations
if args.align_algo in ['pica','ppca']:
new_niter = 10
# load transformation matrices
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
# 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])
#np.savez_compressed(options['working_path']+args.align_algo+'_trfed_lh_'+str(new_niter_lh)+'.npz',trfed_lh = trfed_lh_tmp)
trfed_rh_tmp = transform_rh[:, :, m].T.dot(pred_data_rh[:, :, m])
#np.savez_compressed(options['working_path']+args.align_algo+'_trfed_rh_'+str(new_niter_lh)+'.npz',trfed_rh = trfed_rh_tmp)
if args.align_algo in ['pica','ppca']:
new_niter_lh = new_niter_rh = 10
# 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':
