def load_mat_dataset(datapath, bands, conditions, networks=None):
target_list = []
sample_list = []
chunk_list = []
band_list = []
labels = np.loadtxt(os.path.join(datapath, 'roi_labels.txt'),
dtype=np.str_,
delimiter='\t')
#labels = labels.T[1]
subject_list_chunks = np.loadtxt(os.path.join(datapath, 'subj_list'),
dtype=np.str)
mask = np.zeros(len(labels.T[0]))
if networks != None:
for n in networks:
mask += labels.T[-1] == n
else:
mask = np.ones(len(labels.T[0]), dtype=np.bool_)
mask_roi = np.meshgrid(mask, mask)[1] * np.meshgrid(mask, mask)[0]
for cond in conditions:
for band in bands:
filt_list = [f for f in filelist if f.find(cond) != -1 \
and f.find(band) != -1]
data = loadmat(os.path.join(datapath, filt_list[0]))
mat_ = data[data.keys()[0]]
#mat_[np.isinf(mat_)] = 0
il = np.tril_indices(mat_[0].shape[0])
masked_mat = mat_ * mask_roi[np.newaxis,:]
for m in masked_mat:
m[il] = 0
#samples = np.array([m[il] = 0 for m in masked_mat])
samples = np.array([m[np.nonzero(m)] for m in masked_mat])
targets = [cond for i in samples]
band_ = [band for i in samples]
target_list.append(targets)
sample_list.append(samples)
chunk_list.append(subject_list_chunks)
band_list.append(band_)
targets = np.hstack(target_list)
samples = np.vstack(sample_list)
chunks = np.hstack(chunk_list)
zsamples = sc_zscore(samples, axis=0)
ds = dataset_wizard(zsamples, targets=targets, chunks=chunks)
ds.sa['band'] = np.hstack(band_list)
#zscore(ds)
return ds
#samples = np.array([m[il] = 0 for m in masked_mat])
samples = np.array([m[np.nonzero(m)] for m in masked_mat])
targets = [cond for i in samples]
band_ = [band for i in samples]
target_list.append(targets)
sample_list.append(samples)
chunk_list.append(subject_list_chunks)
band_list.append(band_)
targets = np.hstack(target_list)
samples = np.vstack(sample_list)
chunks = np.hstack(chunk_list)
zsamples = sc_zscore(samples, axis=0)
ds = dataset_wizard(zsamples, targets=targets, chunks=chunks)
ds.sa['band'] = np.hstack(band_list)
zscore(ds)
n_folds = [4]
#n_feats = np.arange(10, 1220, 50)
n_feats = [10]
err_lst = []
sens_mat = []
for k in n_folds:
for n in n_feats: