SYMBOL INDEX (68 symbols across 12 files)

FILE: 01-fetch_data.py
  function str2bool (line 36) | def str2bool(v):
  function main (line 47) | def main():

FILE: 02-process_data.py
  function str2bool (line 31) | def str2bool(v):
  function main (line 42) | def main():

FILE: 03-main.py
  function topk_loss (line 96) | def topk_loss(s,ratio):
  function consist_loss (line 104) | def consist_loss(s):
  function train (line 116) | def train(epoch):
  function test_acc (line 163) | def test_acc(loader):
  function test_loss (line 174) | def test_loss(loader,epoch):

FILE: imports/ABIDEDataset.py
  class ABIDEDataset (line 10) | class ABIDEDataset(InMemoryDataset):
    method __init__ (line 11) | def __init__(self, root, name, transform=None, pre_transform=None):
    method raw_file_names (line 18) | def raw_file_names(self):
    method processed_file_names (line 24) | def processed_file_names(self):
    method download (line 27) | def download(self):
    method process (line 31) | def process(self):
    method __repr__ (line 47) | def __repr__(self):

FILE: imports/gdc.py
  function jit (line 10) | def jit():
  class GDC (line 20) | class GDC(object):
    method __init__ (line 70) | def __init__(self, self_loop_weight=1, normalization_in='sym',
    method __call__ (line 86) | def __call__(self, data):
    method transition_matrix (line 127) | def transition_matrix(self, edge_index, edge_weight, num_nodes,
    method diffusion_matrix_exact (line 173) | def diffusion_matrix_exact(self, edge_index, edge_weight, num_nodes,
    method diffusion_matrix_approx (line 232) | def diffusion_matrix_approx(self, edge_index, edge_weight, num_nodes,
    method sparsify_dense (line 305) | def sparsify_dense(self, matrix, method, **kwargs):
    method sparsify_sparse (line 363) | def sparsify_sparse(self, edge_index, edge_weight, num_nodes, method,
    method __expm__ (line 397) | def __expm__(self, matrix, symmetric):
    method __calculate_eps__ (line 412) | def __calculate_eps__(self, matrix, num_nodes, avg_degree):
    method __neighbors_to_graph__ (line 425) | def __neighbors_to_graph__(self, neighbors, neighbor_weights,
    method __calc_ppr__ (line 456) | def __calc_ppr__(indptr, indices, out_degree, alpha, eps):
    method __repr__ (line 503) | def __repr__(self):

FILE: imports/preprocess_data.py
  function fetch_filenames (line 44) | def fetch_filenames(subject_IDs, file_type, atlas):
  function get_timeseries (line 74) | def get_timeseries(subject_list, atlas_name, silence=False):
  function subject_connectivity (line 95) | def subject_connectivity(timeseries, subjects, atlas_name, kind, iter_no...
  function get_ids (line 144) | def get_ids(num_subjects=None):
  function get_subject_score (line 159) | def get_subject_score(subject_list, score):
  function preprocess_phenotypes (line 188) | def preprocess_phenotypes(pheno_ft, params):
  function phenotype_ft_vector (line 201) | def phenotype_ft_vector(pheno_ft, num_subjects, params):
  function get_networks (line 234) | def get_networks(subject_list, kind, iter_no='', seed=1234, n_subjects='...

FILE: imports/read_abide_stats_parall.py
  function split (line 26) | def split(data, batch):
  function cat (line 53) | def cat(seq):
  class NoDaemonProcess (line 58) | class NoDaemonProcess(multiprocessing.Process):
    method daemon (line 60) | def daemon(self):
    method daemon (line 64) | def daemon(self, value):
  class NoDaemonContext (line 68) | class NoDaemonContext(type(multiprocessing.get_context())):
  function read_data (line 72) | def read_data(data_dir):
  function read_sigle_data (line 128) | def read_sigle_data(data_dir,filename,use_gdc =False):

FILE: imports/utils.py
  function train_val_test_split (line 10) | def train_val_test_split(kfold = 5, fold = 0):

FILE: net/braingnn.py
  class Network (line 14) | class Network(torch.nn.Module):
    method __init__ (line 15) | def __init__(self, indim, ratio, nclass, k=8, R=200):
    method forward (line 52) | def forward(self, x, edge_index, batch, edge_attr, pos):
    method augment_adj (line 77) | def augment_adj(self, edge_index, edge_weight, num_nodes):

FILE: net/braingraphconv.py
  class MyNNConv (line 12) | class MyNNConv(MyMessagePassing):
    method __init__ (line 13) | def __init__(self, in_channels, out_channels, nn, normalize=False, bia...
    method reset_parameters (line 30) | def reset_parameters(self):
    method forward (line 34) | def forward(self, x, edge_index, edge_weight=None, pseudo= None, size=...
    method message (line 58) | def message(self, edge_index_i, size_i, x_j, edge_weight, ptr: OptTens...
    method update (line 62) | def update(self, aggr_out):
    method __repr__ (line 69) | def __repr__(self):

FILE: net/brainmsgpassing.py
  class MyMessagePassing (line 18) | class MyMessagePassing(torch.nn.Module):
    method __init__ (line 40) | def __init__(self, aggr='add', flow='source_to_target', node_dim=0):
    method propagate (line 61) | def propagate(self, edge_index, size=None, **kwargs):
    method message (line 133) | def message(self, x_j):  # pragma: no cover
    method update (line 146) | def update(self, aggr_out):  # pragma: no cover

FILE: net/inits.py
  function uniform (line 4) | def uniform(size, tensor):
  function kaiming_uniform (line 10) | def kaiming_uniform(tensor, fan, a):
  function glorot (line 16) | def glorot(tensor):
  function zeros (line 22) | def zeros(tensor):
  function ones (line 27) | def ones(tensor):