max_voxel=max_sample_voxel,
comm=htfa_comm,
verbose=True)
htfa.fit(train_data, R)
for s in range(n_local_subj):
#get posterior for each subject
subj_idx = mapping[str(s)]
start_idx = s * htfa.prior_size
end_idx = (s + 1) * htfa.prior_size
local_posteiror = htfa.local_posterior_[start_idx:end_idx]
local_centers = htfa.get_centers(local_posteiror)
local_widths = htfa.get_widths(local_posteiror)
htfa.n_dim = n_dim
htfa.cov_vec_size = np.sum(np.arange(htfa.n_dim) + 1)
htfa.map_offset = htfa.get_map_offset()
#training happens on all voxels, but part of TRs
unique_R_all, inds_all = htfa.get_unique_R(R[s])
train_F = htfa.get_factors(unique_R_all, inds_all,
local_centers, local_widths)
#calculate train_recon_err
tmp_train_recon_errs[subj_idx, p, idx] = get_train_err(
htfa, train_data[s], train_F)
#calculate weights on test_weight_data, test_recon_err on test_recon_data
tmp_test_recon_errs[subj_idx, p, idx] = get_test_err(
htfa, test_weight_data[s], test_recon_data[s],
test_weight_R[s], test_recon_R[s], local_centers,
local_widths)
max_voxel=max_sample_voxel,
comm=htfa_comm,
verbose=True)
htfa.fit(train_data, R)
for s in range(n_local_subj):
#get posterior for each subject
subj_idx = mapping[str(s)]
start_idx = s * htfa.prior_size
end_idx = (s + 1) * htfa.prior_size
local_posteiror = htfa.local_posterior_[start_idx:end_idx]
local_centers = htfa.get_centers(local_posteiror)
local_widths = htfa.get_widths(local_posteiror)
htfa.n_dim = n_dim
htfa.cov_vec_size = np.sum(np.arange(htfa.n_dim) + 1)
htfa.map_offset = htfa.get_map_offset()
#training happens on all voxels, but part of TRs
unique_R_all, inds_all = htfa.get_unique_R(R[s])
train_F = htfa.get_factors(unique_R_all,
inds_all,
local_centers,
local_widths)
#calculate train_recon_err
tmp_train_recon_errs[subj_idx, p,idx] = get_train_err(htfa,
train_data[s],
train_F)
#calculate weights on test_weight_data, test_recon_err on test_recon_data
tmp_test_recon_errs[subj_idx, p,idx] = get_test_err(htfa,
