R.append(all_data['R'])
n_voxel, n_tr = data[0].shape
# Run HTFA with downloaded data
from brainiak.factoranalysis.htfa import HTFA
# uncomment below line to get help message on HTFA
#help(HTFA)
K = 5
htfa = HTFA(K=K,
n_subj=n_subj,
max_global_iter=5,
max_local_iter=2,
voxel_ratio=0.5,
tr_ratio=0.5,
max_voxel=n_voxel,
max_tr=n_tr,
verbose=True)
htfa.fit(data, R)
if rank == 0:
print("\n centers of global latent factors are:")
print(htfa.get_centers(htfa.global_posterior_))
print("\n widths of global latent factors are:")
widths = htfa.get_widths(htfa.global_posterior_)
print(widths)
print("\n stds of global latent RBF factors are:")
rbf_std = np.sqrt(widths/(2.0))
print(rbf_std)
R.append(all_data['R'])
n_voxel, n_tr = data[0].shape
# Run HTFA with downloaded data
from brainiak.factoranalysis.htfa import HTFA
# uncomment below line to get help message on HTFA
#help(HTFA)
K = 5
htfa = HTFA(K=K,
n_subj=n_subj,
max_global_iter=5,
max_local_iter=2,
voxel_ratio=0.5,
tr_ratio=0.5,
max_voxel=n_voxel,
max_tr=n_tr,
verbose=True)
htfa.fit(data, R)
if rank == 0:
print("\n centers of global latent factors are:")
print(htfa.get_centers(htfa.global_posterior_))
print("\n widths of global latent factors are:")
widths = htfa.get_widths(htfa.global_posterior_)
print(widths)
print("\n stds of global latent RBF factors are:")
rbf_std = np.sqrt(widths / (2.0))
print(rbf_std)
upper_ratio=upper_ratio,
lower_ratio=lower_ratio,
max_tr=max_sample_tr,
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(
upper_ratio=upper_ratio,
lower_ratio=lower_ratio,
max_tr=max_sample_tr,
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)
