#get fMRI data and scanner RAS coordinates
all_data = scipy.io.loadmat(file_name)
data = all_data['data']
R = all_data['R']
# Z-score the data
data = stats.zscore(data, axis=1, ddof=1)
n_voxel, n_tr = data.shape
# Run TFA with downloaded data
from brainiak.factoranalysis.tfa import TFA
# uncomment below line to get help message on TFA
#help(TFA)
tfa = TFA(K=5,
max_num_voxel=int(n_voxel*0.5),
max_num_tr=int(n_tr*0.5),
verbose=True)
tfa.fit(data, R)
print("\n centers of latent factors are:")
print(tfa.get_centers(tfa.local_posterior_))
print("\n widths of latent factors are:")
widths = tfa.get_widths(tfa.local_posterior_)
print(widths)
print("\n stds of latent RBF factors are:")
rbf_std = np.sqrt(widths/(2.0))
print(rbf_std)
print("File download failed:", e, file=sys.stderr)
#get fMRI data and scanner RAS coordinates
all_data = scipy.io.loadmat(file_name)
data = all_data['data']
R = all_data['R']
# Z-score the data
data = stats.zscore(data, axis=1, ddof=1)
n_voxel, n_tr = data.shape
# Run TFA with downloaded data
from brainiak.factoranalysis.tfa import TFA
# uncomment below line to get help message on TFA
#help(TFA)
tfa = TFA(K=5,
max_num_voxel=int(n_voxel * 0.5),
max_num_tr=int(n_tr * 0.5),
verbose=True)
tfa.fit(data, R)
print("\n centers of latent factors are:")
print(tfa.get_centers(tfa.local_posterior_))
print("\n widths of latent factors are:")
widths = tfa.get_widths(tfa.local_posterior_)
print(widths)
print("\n stds of latent RBF factors are:")
rbf_std = np.sqrt(widths / (2.0))
print(rbf_std)
